Exponential RED A Stabilizing AQM Scheme for Low and by juanagui



  Exponential-RED: A Stabilizing AQM Scheme for
       Low- and High-Speed TCP Protocols
        Shao Liu, Student Member, IEEE, Tamer Basar, Fellow, IEEE, and R. Srikant, Senior Member, IEEE
                       (shaoliu@ifp.uiuc.edu, tbasar@control.csl.uiuc.edu, rsrikant@uiuc.edu)

   Abstract— This paper introduces and analyzes a decentral-                 stable [22], [21]. The main benefit of this algorithm is that it
ized network congestion control algorithm which has dynamic                  achieves arbitrary fairness among the users and leads to full
adaptations at both user ends and link ends, a so-called general             link utilization. This idea was adopted in [30] to modify the
primal-dual algorithm. We obtain sufficient conditions for local
stability of this algorithm in a general topology network with               dual algorithm to allow slow adaptation at the sources and
heterogeneous round-trip delays. Then, as an implementation of               achieve the same benefits as the AVQ algorithm.
this algorithm in the Internet, we introduce an AQM (Active                     Both the modified primal algorithm and the modified dual
Queue Management) scheme called Exponential-RED (E-RED),                     algorithm have dynamic adaptations at both sources and
which outperforms RED and is inherently stable when combined                 routers, and thus can be regarded primal-dual. However, all
with TCP-Reno or its variants for high-speed networks.
   Keywords: Congestion Control, Primal Algorithm, Dual Algo-                the algorithms in the primal family relate the network con-
rithm, Primal-Dual Algorithm, TCP, RED.                                      gestion measure directly with the link aggregate rate, which
                                                                             corresponds to averaging the feedback from the network at the
                                                                             sources; and all the algorithms in the dual family relate the
                         I. I NTRODUCTION                                    source rate directly with the route congestion measure, which
   Recently, there has been a flurry of research activity on de-              corresponds to averaging the source rates at the links before
centralized end-to-end network congestion control algorithms.                the feedback of more explicit congestion information to the
A widely-used framework, introduced in [15], is to associate                 sources [14].
a utility function with each flow and maximize the aggregate                     In this paper, we first briefly review the above developments
system utility function subject to link capacity constraints—an              and identify their role in allocating resources fairly in a network
optimization problem known as Kelly’s System Problem [15].                   of competing users. We then generalize the class of primal-
Congestion control schemes can be viewed as decentralized                    dual algorithms, and provide design guidelines to stabilize
source and router algorithms to drive the system operating                   these algorithms in general topology networks with heteroge-
point to the optimum or some suboptimum solution of this                     neous feedback delays. In this class of algorithms, the source
maximization problem.                                                        dynamics are similar to those in the primal algorithm in [15]
   Congestion control schemes can be divided into three                      and [33] while the link dynamics are similar to those in the dual
classes: primal algorithms, dual algorithms and primal-dual                  algorithm [25]. Following the approach introduced in [33], we
algorithms. In primal algorithms, the users adapt the source                 obtain a local stability result which shows that stability does
rates dynamically based on the route prices, and the links select            not depend on the source adaptation speed, but depends only
a static law to determine the link prices directly from the arrival          on the link adaptation speed. This result subsumes the dual
rates at the links [15]. In dual algorithms, on the other hand, the          algorithm as its limiting case when the source adaptation speed
links adapt the link prices dynamically based on the link rates,             approaches infinity.
and the users select a static law to determine the source rates                 From the stability analysis of the general primal-dual algo-
directly from the route prices and the source parameters [15],               rithm, we also show that RED (Random Early Detection) could
[25], [36]. Primal-dual algorithms combine these two schemes                 stabilize TCP-Reno if modified slightly. Our modification to
and dynamically compute both user rates and link prices [1],                 RED sets the packet marking probability to be an exponential
[35]. For a comprehensive survey of these algorithms, see [31].              function of the length of a virtual queue whose capacity is
   A modified primal algorithm, called the AVQ (Active Virtual                slightly smaller than the link capacity. Due to the exponential
Queue) algorithm, was introduced in [20]. Here the link prices               marking profile, we call it Exponential-RED (E-RED). From
in the original primal algorithm [15] are slowly adjusted so                 our analysis, it can be shown that E-RED stabilizes TCP-Reno
that asymptotically in time, the link prices become equal to                 and all its packet loss/mark based variations. Compared with
the Lagrange multipliers in Kelly’s system problem [20]. More                other queue-length-based AQM schemes, like RED [7], REM
importantly, in the presence of feedback delays, the parameters              [2], PI [12] and BLUE [6], E-RED is the first such scheme that
of this algorithm can be chosen such that the network is locally             can be proved to stabilize TCP-Reno for a general topology
                                                                             network with heterogeneous delays. In parallel with our work,
  An earlier version of this paper appeared in the Proceedings of the 42nd
IEEE Conference on Decision and Control, 2003; see [24].                     another recent paper [10] has also obtained scaling rules for
  Research supported by the NSF ITR Grant CCR 00-85917, DARPA Grant          RED and PI to stabilize TCP-Reno.
F30602-00-2-0542 and AFOSR URI F49620-01-1-0365.                                We finally perform some ns-2 and Matlab simulations to
  Department of Electrical and Computer Engineering and Coordinated Sci-
ence Laboratory, University of Illinois, 1308 West Main Street, Urbana, IL   compare E-RED with RED and to discuss the dependence
61801-2307, USA.                                                             of E-RED’s performance on the network scenario and the E-

RED parameter choices. The simulation results show that E-                  A. Primal Algorithm
RED outperforms RED when combined with TCP-Reno in                             An issue of importance, driven by both theoretical and
the sense that it achieves less queue length oscillation, higher                                                                         x ˆ
                                                                            practical considerations, is the distributed computation of (ˆ, p)
bandwidth utilization, and lower queueing delay at the same                 using only the realistic decentralized information available
time. The simulation results also show that E-RED works well                to individual users. The congestion control problem deals
with HighSpeed TCP [8] and Scalable TCP [18].                               precisely with the tasks of finding such algorithms that are
                II. OVERVIEW OF P RIOR W ORK                                decentralized, and selecting among them the one with the best
                                                                            performance. The users adapt xr with respect to qr , and the
   Consider a set of users/routes, R, and a set of                          links adapt pl with respect to yl . The TCP algorithms are user
links/resources, L. For each user r ∈ R, its route consists                 adaptation laws, and AQM algorithms are link adaptation laws.
of a set of links, which is a subset of L, denoted Lr . Each link              For the logarithmic utility function, Ur (xr ) = wr log xr ,
l ∈ L may be used by several routes; accordingly, we write                  Kelly et al. introduced the Primal Algorithm [15], where each
l ∈ r if l ∈ Lr . Each user r ∈ R has an associated flow rate                user r implements the following algorithm:
xr and a utility function Ur (xr ). Furthermore, each link l ∈ L
has an associated link aggregate rate yl = r∈R:l∈r xr , and                                         ˙
                                                                                                    xr = kr (wr − xr qr ) ,                  (6)
a fixed capacity cl . Introduce the source rates (column) vector             where kr is a scaling factor or the step size. Each Link l, on
x := (xr , r ∈ R), the link rate (column) vector y := (yl , l ∈             the other hand, computes its price as
L), and the routing matrix A := (Alr , l ∈ L, r ∈ R), where
Alr = 1 if l ∈ r and 0 otherwise; then we have y = Ax.                                    pl = fl (yl ), where fl > 0, fl > 0 .              (7)
   We impose the standard conditions on each user’s utility                    Without incorporating delay into the algorithm, it was shown
function Ur (xr ), namely that it be increasing, strictly concave,          in [15] that this primal algorithm is globally stable. If we
and continuously differentiable in xr for xr ≥ 0. Further                   include propagation delay and neglect queueing delay, thus
assume that Ur (xr ) → −∞ as xr → 0, and that the utilities                 treating delay as a constant, then we have the relationships:
are additive so that the aggregate utility for the system is                                                                 f
U (x) = r∈R Ur (xr ). The system problem, first introduced                                       yl (t) =            xr (t − τlr ) ,          (8)
in this context by Kelly [16], is the concave programming
problem:                                                                                                                     b
                                                                                                   qr (t) =         pl (t − τlr ) ,          (9)
         max U (x) subject to y ≤ c over x ≥ 0 ,                      (1)                                     l∈r
           x                                                                         f                                                   b
                                                                            where τlr is the forward delay from source r to link l, and τlr
where c = (cl , l ∈ L), a column vector. The Lagrangian is:
                                                                            is the backward delay from link l back to source r, and we
                                                                                                           f    b
           L(x, p) =         Ur (xr ) +         pl (cl − yl ) ,       (2)   have round trip delay Tr := τlr + τlr , for any l ∈ r.
                       r∈R                l∈L                                  Denote the Laplace transforms of x(t), y(t), p(t), and q(t),
where pl is the Lagrange multiplier associated with link l, l ∈             respectively, by X(s), Y (s), P (s), and Q(s), and define the
L. This multiplier is the shadow price of the link, and in many             routing matrix with delay in the frequency domain as A(s) =
algorithms it also summarizes the link congestion information.              (Alr (s), l ∈ L, r ∈ R) , where
Associate with each user r an aggregate route price qr defined                            Alr (s)     =          f
                                                                                                         exp(−sτlr )          if l ∈ r ,
as qr := l∈r pl . Introduce the link price (column) vector p =
                                                                                                     =   0                    otherwise.
(pl , l ∈ L) and the route price (column) vector q = (qr , r ∈ R).
Then we have q = AT p, and noting that pT y = pT Ax =                       Then we have the relationships [33]
xT AT p = xT q, the Lagrangian can be rewritten as:
                                                                                                     Y (s) = A(s)X(s) ,                     (10)
          L(x, p) =         (Ur (xr ) − xr qr ) +          pl c l .   (3)                  Q(s) = diag(e−sTr )AT (−s)P (s) ,                (11)
                      r∈R                            l∈L
From the Kuhn-Tucker theorem [3] of nonlinear programming,                  and the primal algorithm with delay is given as follows [13]:
ˆ                                            ˆ
x solves (1) if and only if there exists a p such that the pair                  User end:      ˙
                                                                                               xr (t) = kr (wr − xr (t − Tr )qr (t)) .      (12)
 x ˆ
(ˆ, p) constitutes a saddle point for the function L(x, p), where
x is the maximizer and p is the minimizer, with x ≥ 0 and                                    Link end:        pl (t) = fl (yl (t)) .        (13)
p ≥ 0. Making the natural assumption that the constraint set                Conditions for local stability were first conjectured in [13], and
for x is not empty and is bounded (and thus compact), the strict            then proved and extended in [32], [33], [34]. For a certain class
concavity of the aggregate utility function leads to the existence          of functions fl (·), global stability has been established in [37].
of a unique optimal solution x to (1). This then guarantees the                Note that the link price computation in the primal algorithm
              ˆ                             ˆ
existence of p, and thus the existence of q . The uniqueness of             above is static. An algorithm called AVQ was introduced
ˆ                                 ˆ
x determines the uniqueness of q . If A has full row rank, then             in [20], where users choose the same dynamic adaptation, but
p is uniquely determined from q, and thus p is also unique. A               at the links, pl (t) is a function of both yl (t) and a virtual queue
      x ˆ
pair (ˆ, p) is in saddle-point equilibrium if and only if [16]              capacity cl which is a time-varying link parameter. The global
                    Ur (ˆr ) = qr ∀r ∈ R ,
                        x      ˆ                                      (4)   stability of this algorithm has been established in [23], [35]
                                                                            without delay, and local stability has been established in [22],
    ˆ        ˆ            ˆ                ˆ
    pl (cl − yl ) = 0 and pl ≥ 0, and cl ≥ yl ∀l ∈ L .                (5)   [21] in the presence of feedback delays.

B. Dual Algorithm                                                       and
                                                                                                = cl     if   pl > 0 ,
   The dual algorithm introduced in [25] employs the following                            yl                                        (19)
                                                                                                ≤ cl     if   pl = 0 ,
algorithms at the link and user ends:
                                                                        for all l, r. So the equilibrium point solves the system problem
                   Links:    ˙
                             pl = γl (yl − cl ) ,                (14)   (1) exactly. In [35], a passivity approach was used to show
where γl > 0 is an adaptation parameter. In [30], γl was chosen         global stability of this equilibrium without considering delay.
to be 1/cl .                                                            In the next section, we will embed this algorithm within a more
                                                                        general class of primal-dual algorithms, and for this general
              Users:    xr = xr (qr ) = Ur             (qr ) .   (15)   class we will establish local stability in the presence of delay.
The above dual algorithm is a special case of the dual algorithm        D. Remarks on the Link Model
in [15], [14]. It was also derived in [36] for the special case
                                                                           As we have already stated, most of the AQM algorithms
of logarithmic utility functions.
                                                                        choose the marking probability as a function of the queue
   For this dual algorithm, global stability was proved in [25],
                                                                        length. So it is intuitive to choose pl to be a function of
while local stability was proved for a particular choice of
                                                                        the queue length bl , and not as a function of the aggregate
utility functions in [29]. The restriction on the choice of utility
                                                                        link arrival rate yl . This is the model chosen by the dual and
functions means that arbitrary relative fairness among the users
                                                                        primal-dual approaches. However, in the primal approach, pl
cannot be achieved using the algorithm in [29]. Following the
                                                                        is chosen to be a function of yl . Does this then mean that
time-scale decomposition idea in [23], [22], [21], to track an
                                                                        one has to measure the link arrival rate, and not the queue
arbitrary utility function, the dual algorithm was modified in
                                                                        length, to compute the marking probability? In general, the
[30] by introducing slow time-scale dynamics at the user end.
                                                                        answer seems to be no when there are stochastic disturbances
In other words, xr = xr (ξr , qr ), where ξr is a slowly adjusted
                                                                        in the network. In [17], Kelly argues that if the queue length
source parameter, just like the virtual queue capacity cl in the
                                                                        hits zero several times within a round-trip time, even if the
AVQ algorithms. This ensures that the dual algorithm can also
                                                                        marking is queue-length-based, the source acts as though the
allocate the network resources fairly.
                                                                        marking probability is a function of the stochastically averaged
                                                                        version of the queue length. Thus, in the deterministic model
C. Primal-Dual Algorithm                                                of congestion control, the source only sees the price pl as a
   Both the primal algorithms and the dual algorithms have              static function of yl . However, the conditions under which the
dynamics at one end (user end for the primal algorithm and link         queue length hits zero frequently have not been provided in
end for the dual algorithm) and static adaptation at the other.         [17]. Recently, in [4], it has been shown that the parameters of
However, in the current Internet, TCP is a dynamic source               popular AQM schemes determine whether the queue length hits
algorithm, and most AQM algorithms, like RED, Vegas, etc,               zero frequently or not. Specifically, if both the queue length and
relate the price to the queue length or queueing delay, and             the capacity are scaled in proportion to the number of users
thus are dynamic link algorithms. This motivates the work on            in the network, and the marking probability is based on the
primal-dual algorithms, to directly relate xr to qr and pl to yl .
                                           ˙             ˙              queue length divided by the number of users in the network,
   As already mentioned, x solves the system problem if and
                           ˆ                                            then we have a queue-based model; else, the queue length hits
only if there exists a p such that (ˆ, p) is the saddle point of
                       ˆ            x ˆ                                 zero frequently and we have the rate-based model. Thus, the
L(x, p). So we can approach this optimization problem from a            primal-dual algorithm considered here requires that the AQM
game theoretical point of view: L(x, p) is the gain for player 1        parameters are chosen such that the queue length does not hit
(P 1) who controls x, and loss for player 2 (P 2) who controls          zero frequently. Since we also would like to have negligible
p. So P 1 is the maximizer and P 2 is the minimizer, and we             queueing delay, this suggests that the AQM algorithm based
have a 2-player zero-sum game, with (ˆ, p) the unique saddle
                                         x ˆ                            on the primal-dual algorithm analysis must adjust its marking
point:                                                                  probability according to the occupancy of a virtual queue,
                                                                        whose capacity is slightly smaller than that of the real link
            ˆ      x ˆ       x
       L(x, p) ≤ L(ˆ, p) ≤ L(ˆ, p)           ∀ x ≥ 0, p ≥ 0 .           [9]. Thus, we can maintain a large virtual queue, which always
To reach this unique saddle point equilibrium, we can choose            stays nonzero, as well as a small real queue, yielding negligible
iterative algorithms with dynamic adaptations from both play-           queueing delay. We will discuss this further in later sections.
ers. The simplest such algorithm is the gradient algorithm:                III. A G ENERAL P RIMAL -D UAL A LGORITHM AND I TS
                    ∂L(x, p)                                                                  L OCAL A NALYSIS
            xr = kr          = kr (Ur (xr ) − qr ) ,             (16)
                      ∂xr                                                  The primal-dual algorithm (16)-(17) is too specific and still
                          ∂L(x, p)                                      far from being interpreted as a TCP-AQM combination. We
               pl = −γl            = γl (yl − cl ) .             (17)   introduce here a general primal-dual algorithm, whose source
                                                                        law includes current versions of TCP as special cases and
Such algorithms were considered in [1] and [35], which can be           whose link law includes most current AQM algorithms as
regarded as primal-dual algorithms in the sense that xr depends         special cases. Define [x]+ := max(0, x) and
on qr and pl depends on yl . At equilibrium, we have:
                                                                                                         h(x)    if x > 0 ,
                                   −1                                              [h(x)]+ :=
                        xr = U r        (qr ) ,                  (18)                    x             [h(x)]+   if x = 0 .

  The general primal-dual algorithm is given by                                  Let us now linearize (22). At the equilibrium point, we have
                                                    +                         ˆ         x
                                                                              qr = Ur (ˆr ). Linearization around it gives:
                   xr = fr (xr ) Ur (xr ) −
                   ˙                             qr x      ,          (20)
                                                                                             xr (t) = fr (Ur (ˆr )xr (t) − qr (t)) ,
                                                                                             ˙                x
                       pl = gl (pl ) yl −     cl p     ,              (21)              ˆ
                                                   l                                              x ˆ
                                                                              where fr = fr (ˆr , xr ). Note that in the above equation and
where fr (xr ) and gl (pl ) are continuous functions, lower                   in the rest of the paper, by slightly abusing notation, we use
bounded by constants fr > 0 and gl > 0, respectively.                         xr , qr , yl , and pl also to represent the perturbations from their
                                                                                                      ˆ ˆ ˆ           ˆ
                                                                              equilibrium values xr , qr , yl , and pl , respectively.
A. Global Stability without Delay                                                In the Laplace domain, we have

  It immediately follows that the equilibrium point of the                                          ˆ           ˆ      x
                                                                                         sXr (s) = −fr Qr (s) + fr Ur (ˆr )Xr (s) .
general primal-dual algorithm (20)-(21) solves the system
                                                                              Solving for Xr (s), yields:
problem exactly. Using an approach similar to the one in [35],
one can show that the general primal-dual algorithm without                             ˆ           1                           1        θr
                                                                              Xr (s) = −fr                     Qr (s) = −                      Qr (s) ,
delay is globally asymptotically stable (GAS). Define1                                            ˆ
                                                                                             s − fr Ur (ˆr )
                                                                                                        x                        x
                                                                                                                             ur (ˆr ) sTr + θr
                                      xr                                                                                                        (24)
                                           u − xr
                    Hr (xr ) :=                   du ,                        where
                                    ˆ      fr (u)                                                        ˆ           x
                                                                                                    θr = fr Tr (−Ur (ˆr )) .                    (25)
                                           v − pl                             Note that θr > 0, since Ur (xr ) is strictly concave and thus
                     Jl (pl ) :=                  dv ,
                                    pl     gl (v)                             Ur (xr ) < 0.
and introduce                                                                   For the link dynamics, linearization yields:

               V (x, p) =          Hr (xr ) +          Jl (pl ) .                                        ˙        p
                                                                                                         pl = gl (ˆl )yl .
                            r∈R                 l∈L
                                                                              In the Laplace domain, we have:
  Using these, we can establish the following theorem on
                                                                                                            gl (ˆl )
global stability of the system; see [31] for a proof.                                               Pl (s) =         Yl (s) .        (26)
Theorem III.1. V (x, p) is a Lyapunov function for the sys-                     The return ratio of the closed-loop feedback system (10),
tem described by (20)-(21), and thus the system is globally                   (11), (24) and (26) is given by
asymptotically stable.
                                                                                               1      θr e−sTr                 p
                                                                                                                           gl (ˆl )
                                                                              L(s) = diag                      AT (−s)diag          A(s) .
                                                                                           −Ur (ˆr ) sTr + θr
                                                                                                 x                            s
B. Local Stability Analysis with Delay                                                                                                (27)
  With feedback delay, we can modify (20)-(21) to:                            Notice that we can rewrite L(s) as:

   xr (t) = fr (xr (t), xr (t − Tr ))[Ur (xr (t)) − qr (t)]+r ,
   ˙                                                                  (22)                                  Tr        θr e−sTr
                                                           x                            L(s)    =    diag
                                                                                                         −Ur (ˆr ) sTr (sTr + θr )
                                                                                                               x                                (28)
                   pl (t) = gl (pl (t))[yl (t) − cl ]+ ,
                   ˙                                 pl               (23)                           ×AT (−s)diag(gl (ˆl ))A(s) .
where, by slightly abusing notation, we allow fr to be a                      This now leads to the following theorem on stability, whose
continuous function of both xr (t) and xr (t − Tr ), which is                 proof can be found in the Appendix.
again lower bounded by the constant fr > 0.
                                                                              Theorem III.2. The closed-loop system described by (22),
   Before we proceed with the local analysis by linearizing
                                                                              (23), (8), and (9) is locally asymptotically stable around the
the algorithm around the equilibrium values, we first note the
                                                                              equilibrium point if the following two conditions hold:
following on the boundary conditions in (22) and (23) and
the constant delay assumption in the system modelling. For                                                           ˆ
the users, xr > 0, ∀r ∈ R, since Ur (xr ) → ∞ as x → 0.
            ˆ                                                                                                x
                                                                                                        −Ur (ˆ) ≥          ,                    (29)
                                                                                                                    ar x r
For the links, we restrict the link set L to the active link set
La := {l ∈ L : pl > 0}, i.e., we consider only the bottleneck                                            p
                                                                                                     gl (ˆl )    1 1
                                                                                                              ≤           ,                     (30)
links. Since the set L \ La does not affect the local analysis,                                         ˆ
                                                                                                        pl      2cl al Tm
for the rest of this paper we will assume that L = La . Then,                 where ar is a positive constant for each r, and al =
the boundary conditions in (22) and (23) can be ignored for                   maxr∈l ar , Tm = maxr∈R Tr .
local analysis. Also, we assume that the queueing delay can
                                                                              Remark 1. Condition (29) is satisfied for the general class of
be neglected when compared with propagation delay, and thus
                                                                              utility functions introduced in [28]. For example, if Ur (xr ) is of
the round trip time can be taken to be a constant. This is a
                                                                              the form wr log xr (logarithmic utility function), we have ar =
reasonable assumption if one assumes that each router uses a
                                                                              1. If Ur (xr ) is of the form −wr /xnr (power utility function of
virtual queue to compute link prices [9].
                                                                              order nr ), we have ar = 1/(1 + nr ).
   1 The definitions of H (x ) and J (p ) were suggested by Murat Arcak
                        r r        l l
in his correspondence with John Wen and the third author of this paper in a   Remark 2. From (28) and the proof of Theorem III.2 in the
different context.                                                            Appendix, we see that the sufficient condition for local stability

(l.s.) depends only on the link price adaptation speed gl (pl )           and
                                                                                                         mr ir −dr     i
and the user utility function Ur (xr ), and not on the source                           Ur (xr (t)) =      x       (t)Tr r −dr .
                                                                                                         nr r
rate adaptation speed fr (xr (t), xr (t − Tr )). If we adapt the
source rate infinitely fast (i.e., choosing fr (·) ≡ κr and letting        Thus,
κr → ∞), we arrive at the dual algorithm. The corresponding               ˆ                                                             ir − d r
                                                                          fr = nr xdr +1 Tr r −1
                                                                                  ˆr                 and    Ur (xr (t)) = Ur (xr (t))
sufficient condition for stability of the dual algorithm also                                                                             xr (t)
depends only on the link adaptation speed and the user utility
                                                                          So, we have
function. If gl (pl ) is fixed to be 1/cl as in the dual algorithm
[25], then the utility function must be specified as in [29]. To                                       q
                                                                                            (dr − ir )ˆr                       1
                                                                                Ur (ˆr ) = −                  and     ar =          .
allow arbitrary utility functions, we could either add a slow                                    ˆ
                                                                                                 xr                        dr − i r
adaptation at the source [30] or choose a different gl (pl ).             In particular, TCP-Reno and HighSpeed TCP correspond to a
Thus, the interesting observation from our analysis is that slow          power utility function of order 1 and ar = 1/2, while Scalable-
adaptation at the source (as in [30]) is not necessary for the            TCP corresponds to a logarithmic utility function and ar = 1.
dual algorithm to be stable, and achieve full utilization and
arbitrary fairness. The dual algorithm can also be stabilized             B. Exponential RED (E-RED)
with slow adaptation at the links since the l.s. result does not
                                                                            From Theorem III.2, we know that to stabilize the window
depend on fr (xr ).
                                                                          adaptation source law in the TCP family, the link dynamic
    IV. I MPLEMENTATION IN THE I NTERNET AND E-RED                        adaptation should satisfy
                         AQM S CHEME                                                                   p
                                                                                                   gl (ˆl )       1
                                                                                                            ≤           .                   (33)
   In the current Internet, the source law is TCP and the link                                        ˆ
                                                                                                      pl      2al Tm cl
law is an AQM scheme, such as DropTail or RED. We wish                      Consider the link dynamics
to make as little changes to the current protocols as possible,                                               pl
but still achieve guaranteed stability and better performance. In                          ˙
                                                                                           pl = max(gl , βl      )[yl − cl ]+ ,
                                                                                                                            pl              (34)
this section we utilize the local stability analysis for the primal-
                                                                          with βl < 1/(2al Tm ). It satisfies the stability condition (33)
dual algorithm to modify TCP+RED such that the combination
                                                                               ˆ                                             ˆ
                                                                          if pl is not close to zero (more specifically, pl > gl cl /βl ).
is locally stable.
                                                                          Throughout this paper, we assume that the above condition on
                                                                          pl is satisfied. Equation (34) can be implemented at the packet
A. General Form of TCP
                                                                          level by setting the packet marking probability (the link price)
   Consider the general form of TCP introduced in [33], which             pl as an exponential function of the virtual queue length bl :
contains all possible TCP variants. Let Wr represent the                          
window size of source r. Suppose the window size increases                         0
                                                                                                              if 0 ≤ bl < thmin,l ,
         i                                                                 pl =                 (bl −thmin,l )
by mr Wr r for each unmarked acknowledgement and decreases
                                                                                   pmin,l e                   if thmin,l < bl < thmax,l ,

by nr Wr for each marked acknowledgement, where mr , nr ,                          1                          if bl ≥ thmax,l ,
ir and dr are constants, with mr and nr being positive and                                                                              (35)
ir < dr being both integers. We note that the choice of ir = −1           where thmin,l < thmax,l are the two queue length thresholds
and dr = 1 corresponds to TCP-Reno (mr = 1, nr = 2/3 for                  between which the exponential marking is selected and pmin,l
the standard version), and the choice of ir = 0 and dr = 1                is the marking threshold when bl = thmin,l . Let pmax,l denote
corresponds to the Scalable TCP in [18].                                  the marking probability when bl = thmax,l ; then, thmin,l ,
   This window based adaptation can be written in differential            thmax,l , and pmin,l must be such that pmax,l < 1. If pmin,l and
equation form as                                                                                                                ˆ
                                                                          pmax,l are selected properly such that pmin,l < pl < pmax,l ,
      ˙                    i             d                                then between the two probability thresholds, the derivative of
     Wr = xr (t − Tr )(mr Wr r (t) − nr Wr r (t)qr (t)) .         (31)
                                                                          pl in (35) is just (34) and the linearization satisfies (33). Then,
We note here the implicit assumption that qr is sufficiently               this marking scheme locally stabilizes TCP given that the fluid
small so that we do not distinguish between 1 and 1 − qr (t).             model of the system dynamics is valid. We call the above
This is widely accepted in the modelling of TCP ([19], [27]).             marking scheme Exponential-RED or E-RED in short, since
  The source rate is xr = Wr /Tr , so we have:                            the marking profile is an exponential function of the queue
            xr (t − Tr )                                                  length. We note that the form of (35) is similar to the fair dual
   ˙    =                   mr Tr r xir (t) − nr Tr r xdr (t)qr (t)
                                                       r                  algorithm in [14]. However, the source control laws are static
       = n r Tr  dr −1               dr
                       xr (t − Tr )xr (t)                                 in the fair dual algorithm, and hence the stability conditions in
            ×( mr xir −dr (t)Tr r −dr − qr (t)) .
                  r             i                                         [14] are different than the ones in this paper.
                n    r
                                                                   (32)       In the TCP-AQM scheme, the prices pl and qr are expressed
Comparing this with the source law (22) of the primal-dual al-            in terms of the marking probabilities, which is why we use
gorithm, and considering the fact that fr (xr ) is lower bounded          pl , qr to represent both the prices and the marking probabilities.
by fr > 0 and that (32) represents the dynamics of TCP only               However, the price feedback from pl to qr is linear in (9). while
when xr is not close to zero, we have:                                    the probabilistic feedback is actually not linear:
fr (xr (t), xr (t − Tr )) = max(fr , nr xr (t)xdr (t − Tr )Tr r −1 ) ,
                                                                                               qr = 1 −        (1 − pl ) .                  (36)

We notice, however, that if pl and qr are sufficiently small for    ing loop function:
all l ∈ L, r ∈ R, we can approximate (36) by (9). In this
                                                                                                Tr        θr e−sTr
and the next subsection, we assume that the small probability          L(s)     =      diag                           AT (−s)
condition holds; subsequently in subsection IV-D, we consider                                        x
                                                                                             −U (ˆr ) Tr (sTr + θr )
                                                                                              βl p l   κl
the stability issue for the general case.                                              ×diag                    A(s)
                                                                                               cl s(s + κl )
   Given the linear feedback from pl to qr , from Theorem III.2,
                                                                                                Tr        θr e−sTr
the condition βl < 1/(2al Tm ) guarantees the local stability of                =      diag                            AT (−s)
E-RED combined with any window adaptation source law in                                              x
                                                                                             −U (ˆr ) sTr (sTr + θr )
                                                                                              β l p l κl
the TCP family. Note that E-RED simply uses a marking profile                           ×diag                 A(s)
different than RED, and employs a virtual queue.                                               cl s + κ l
Remark 3. The slow link adaptation makes the queue length            This now leads to the following theorem on the stability
very large. To maintain a small queue length, so that the          condition for the source algorithm (20) and E-RED-aq:
queueing delay is negligible and thus the RTT can be modeled       Theorem IV.1. The closed-loop system composed of the source
as a constant, we need to implement the E-RED algorithm in         algorithm (20) and E-RED-aq is locally asymptotically stable
a virtual queue whose capacity is cl = γl cl , where 0 < γl < 1    around the equilibrium if the two conditions in (29) and (30)
is the link utilization parameter. Then, bl is the queue length    are satisfied.
in that virtual queue, and we can have a large bl while
maintaining a small real queue.                                      Proof: See the Appendix.
                                                                     This theorem suggests that E-RED-aq requires the same link
                                                                   adaptation as E-RED-iq, so the averaging of the queue length
C. E-RED with the Average Queue Length                             as in RED is not detrimental to stability.
   In RED, the marking probability is a linear function of the
average queue length. In E-RED, we can also take the marking       D. Probabilistic Feedback
probability to be an exponential function of the average value        In this subsection, we will show that even under exact
of the virtual queue length. We call this variant E-RED-aq and     probabilistic feedback, the local stability results still hold. We
the original E-RED without averaging E-RED-iq (iq stands for       introduce two pseudo price variables vr and zl , defined as:
instantaneous queue length).
                                                                              vr = − log(1 − qr ) ,          zl = − log(1 − pl ) .
   For this variant, we replace bl with rl in the marking
equation (35), where rl is the exponentially weighted moving       Then (36) leads to the linearized relationship:
average of bl based on samples taken every δl seconds. Thus,
                                                                                          vr (t) =         zl (t − τlr ) .           (42)
           rl ((k + 1)δl ) = r(kδl )(1 − αl ) + αl bl ,    (37)                                      l∈r

                                                                   Let v and z be the column vectors of all vr and zl , respectively,
where 0 < αl < 1 is the weight. As in [27], we use the             and let V (s) and Z(s) be Laplace transforms of v(t) and z(t).
following continuous approximation to (37):                        Then, we have a relationship similar to (9):
                      rl = −κl (rl − bl ) ,
                      ˙                                    (38)                     V (s) = diag(e−sTr )AT (−s)Z(s) .                (43)

where κl = − log(1 − αl )/δl . In the Laplace domain, we have:     Comparing (28) with (41) and noticing that for E-RED,
                                                                   gl (pl ) = βl pl /cl , we see that the loop function of either E-
                                 κl                                RED-iq or E-RED-aq combined with (20) can be written as
                   r(s) =               yl (s) .
                             s(s + κl )
                                                                                     xr ˜                          ˆ
                                                                     L(s) = diag        fr (s)e−sTr AT (−s)diag       ˜
                                                                                                                      gl (s) A(s) ,
The dynamics of E-RED-aq yields                                                       ˆ
                                                                                     qr                            cl
                                      βl                                    ˜           ˜
                                                                   where fr (s) and gl (s) are two functions of s. The stability
                           pl = p l      ˙
                                         rl .
                                      cl                           results build on the fact that the spectral radius of the matrix
                                                                   diag( pl )R(s)diag( xr ) is bounded by 1, and thus we can
Since at equilibrium rl = 0, linearization yields:
                                                                   select the parameters such that the eigenloci of L cross the real
                                  βl p l
                                     ˆ                             line to the right of −1. Then, the stability condition is obtained
                           pl =          ˙
                                         rl ,              (39)    from the generalized Nyquist Criterion.
                                                                      Now, for the probabilistic feedback, with the relationships
and in the Laplace domain, we have:                                (10) and (43), we have:
                           βl p l
                              ˆ    κl                                                         xr ˜
                                                                                              ˆ               xr ˜
                                                                                                              ˆ         ∂qr
                                                                              Xr (s)    =     qr fr (s)Qr (s) qr fr (s) ∂vr Vr (s)
                Pl (s) =                  Yl (s) .         (40)                               ˆ
                                                                                              xr ˜
                            cl s(s + κl )                                               =                   ˆ
                                                                                              qr fr (s)(1 − qr )Vr (s) ,

  Combining the source algorithm (20) with logarithmic or                     ∂zl             1                  1      ˆ
                                                                   Zl (s) =       Pl (s) =           Pl (s) =              ˜
                                                                                                                           gl (s)Yl (s) .
power utility function and E-RED-aq, we arrive at the follow-                 ∂pl               ˆ
                                                                                           (1 − pl )               ˆ
                                                                                                              (1 − pl ) cl

                                                                            S1                                                           D1
So the new loop function is in the following form:                                    d 11                                     d12
                                                                            S2                                                           D2
      L(s)    =        ˆ      q   ˜
                  diag xr (1−ˆr ) fr (s)e−sTr AT (−s)                                         R1                          R2             D3
                                                              (45)          S3                              d
                  ×diag cl (1−pl ) gl (s) A(s) .
                                ˆ ˜

Thus for the probabilistic feedback law (42), the same stability            SN         d N1                                    dN2
conditions as those in the price feedback law (9) are obtained
if we could prove that the spectral radius of the matrix              Fig. 1. The network topology: a single bottleneck link and multiple flows with
diag( cl (1−pl ) )A(s)diag( xr (1−ˆr ) ) is also bounded by 1.
                                    q                                 heterogeneous delays

Since r:r∈l xr ≤ cl , ∀l, we just need to show that
                            pl        qr
                                 ≤                         (46)
                          1 − pl    1 − qr                            that RED becomes unstable when the capacity or the round
                                                                      trip delay becomes large. In the first simulation scenario,
Theorem IV.2. The inequality (46) holds, and thus the stability       we choose a large link capacity network with c = 1 Gbps,
results for the linear price feedback case also hold under            N = 2000, d = 10ms, and the delays of all other links
probabilistic feedback.                                               being uniformly distributed between 1 and 20 ms. So the
  Proof: See the Appendix.                                            maximum round trip time is Tm = 100ms. For the E-RED
                                                                      parameters, we set pmin,l = 0.0005, pmax,l = 0.1, γ = 0.95,
                       V. S IMULATIONS                                ξ = 1. Even though our sufficient stability condition suggests
                                                                      ξ ≤ 1/2, we have chosen ξ = 1 in order to understand how
    We implemented the E-RED algorithm in the ns-2 package            conservative our stability results are. For RED, we monitor
and performed simulations on a network whose sources use              the instantaneous queue length, and measure the throughput
TCP-Reno, and whose routers choose E-RED-iq, E-RED-aq or              with a time granularity of 0.1s; they are shown in Fig. 2.
RED. Let ξl := βl Tm /2; then, ξl ≤ 1/2 is the local stability        From the graphs, we see that RED is unstable in the sense
condition for E-RED-iq with TCP-Reno. We make all sources             that both the queue length and the throughput show significant
ECN-capable, and set the maximum window size to be 1000               oscillations, and the queue length hits zero and the full buffer
instead of 64, so the maximum window size will not be a               size frequently. For E-RED, we monitor the real queue length
threshold for the flow rate in all scenarios. We also let all          and the virtual queue length, and measure the throughput with
sources send packets of identical size, 1040 bytes, including         the same time granularity; they are shown in Fig. 3. From the
the IP and TCP header. For E-RED, we choose the parameters            graphs, we see that E-RED is stable in the sense that the virtual
ξl , pmin,l , pmax,l , thmin,l , γl , and αl (αl = 1 corresponds to   queue length oscillates around its equilibrium very slightly, the
E-RED-iq and αl < 1 corresponds to E-RED-aq), and compute             throughput is less oscillatory than that of RED, and the real
thmax,l from pmin,l , pmax,l , thmin,l .                              queue size is always small after the transient phase. So E-
    For most simulations in this section, we consider a network       RED stabilizes TCP-Reno in large capacity scenarios while
with a single bottleneck link and multiple flows, as shown in          RED does not. To compare the performances numerically,
Fig. 1. Since there is a large population of users in the Internet    we measure the average queue length (Avg(qlen)), standard
and the fluid model makes sense only when the number of                deviation of queue length (Std(qlen)), and average throughput
users is large, we choose a sufficiently large number of users         (Avg(thr)) for both algorithms after the transient phase (over
for most of the simulations. We vary the values of N , c, d,          the interval between 10s and 39s for this scenario); these values
d11 to dN 2 to obtain different network configurations. We set         are listed in Table I. In the second scenario, we choose a large
the buffer limit (of the real queue) always to be max(0.3c, 25)       round trip delay network with c = 300 Mbps, N = 1000,
packets, where c is measured in Mbps, which guarantees that           d = 10ms, and the delays of all other links being uniformly
the bottleneck queueing delay for sufficiently large capacity          distributed between 45 and 95 ms. So the maximum round trip
is bounded by 2.5ms (actually, we will see that if E-RED              time is Tm = 400ms. We set pmin,l = 0.00067, pmax,l = 0.1,
stabilizes the system, then the real queue length is much             γ = 0.95, ξ = 1. The queue length and throughput for RED
smaller than its buffer limit and thus the queueing delay is          are shown in Fig. 4, and we see that RED is unstable. The
much smaller than this bound). As for the parameter settings,         real queue length, virtual queue length and throughput for E-
for RED, we set thresh to be 1/5 of the buffer limit and              RED are shown in Fig. 5; we see that E-RED is stable and
maxthresh to be 3 ∗ thresh, keep all the other parameters             achieves a higher and less oscillatory throughput and lower and
at their default settings, and choose ECN marking instead of          less oscillatory queueing delay than RED. For the numerical
dropping; for E-RED, we set thmin,l to be 1/5 of the buffer           comparison, the average throughput, average queue length, and
limit (of the real queue). The other parameters for E-RED will        standard deviation of queue length (measured after the transient
be varied for different scenarios.                                    phase) for both algorithms are listed in Table I.
                                                                         So for both large capacity and large delay networks, given
A. Stability and Performance Comparison of RED and E-RED              that the number of users is sufficiently large, E-RED stabilizes
                                                                      TCP-Reno while RED does not. Furthermore, E-RED achieves
  We first compare the stability and performances of E-RED             higher bandwidth utilization and smaller queueing delay.
and RED. From the analysis in [11] and [26], we know                     One may argue that the advantage of E-RED over RED is

                                                                                                     Queue Length (packets) Vs Time (seconds)                                                                                                                                                           Queue Length (packets) Vs Time (seconds)
                                            300                                                                                                                                                                                                             90


                                            200                                                                                                                                                                                                             60
queue length (packets)

                                                                                                                                                                                                                                   queue length (packets)

                                            100                                                                                                                                                                                                             30


                                                       0                                                                                                                                                                                                    0
                                                           0                   5               10           15             20             25             30            35        40                                                                              0              10               20                30             40             50               60          70         80
                                                                                                                      time (seconds)                                                                                                                                                                                         time (seconds)
                                                                                                           Throughput (bps) Vs Time (seconds)                                                                                                                                                                     Throughput (bps) Vs Time (seconds)
                                                      1.2e+09                                                                                                                                                                                               3.5e+08


Throughput (bps)

                                                                                                                                                                                                                                   Throughput (bps)





                                                                   0                                                                                                                                                                                                   0
                                                                       0               5            10           15           20           25              30          35        40                                                                                        0              10            20              30            40              50           60         70         80
                                                                                                                        time (seconds)                                                                                                                                                                                          time (seconds)

                                                                                                                                                                                       Fig. 4. RED in a large delay network: instantaneous queue length (top graph)
Fig. 2. RED in a large capacity network: instantaneous queue length (top                                                                                                               and throughput (bottom graph).
graph), and throughput (bottom graph).

                                                                                                    Real Queue Length (packets) Vs Time (seconds)                                                                                                                                        Real Queue Length (packets) Vs Time (seconds)
                                                      300                                                                                                                                                                 90
                                                                                                                                                         ’vqresult’ using 1:2

                     real queue length (packets)

                                                                                                                                                                                         real queue length (packets)

                                                      200                                                                                                                                                                 60


                                                      100                                                                                                                                                                 30


                                                           0                                                                                                                                                              0
                                                               0                   5            10            15              20           25              30            35       40                                           0                                 20            40          60           80          100      120              140          160          180    200
                                                                                                                        time (seconds)                                                                                                                                                                        time (seconds)

                                                                                                     Virtual Queue Length (packets) Vs Time (seconds)                                                                                                                                     Virtual Queue Length (packets) Vs Time (seconds)
                                                       30000                                                                                                                                                              45000
                                                                                                                                                         ’vqresult’ using 1:3

                     virtual queue length (packets)

                                                                                                                                                                                         virtual queue length (packets)

                                                       20000                                                                                                                                                              30000


                                                       10000                                                                                                                                                              15000


                                                               0                                                                                                                                                                   0
                                                                       0               5            10           15            20              25             30         35       40                                                        0                         20            40          60           80         100      120          140          160          180    200
                                                                                                                         time (seconds)                                                                                                                                                                           time (seconds)

                                                                                                            Throughtput (bps) Vs Time (seconds)                                                                                                                                                      Throughtput (bps) Vs Time (seconds)
                                                       1.2e+09                                                                                                                                                            3.5e+08
                                                                                                                                                    ’vqthroughput’ using 1:2


                     throughput (bps)

                                                                                                                                                                                         throughput (bps)






                                                                       0                                                                                                                                                                            0
                                                                           0               5          10           15           20             25             30            35    40                                                                        0          20            40          60           80         100         120         140        160         180    200
                                                                                                                          time (seconds)                                                                                                                                                                           time (seconds)

                                                                                                                                                                                       Fig. 5. E-RED in a large delay network: real queue length (top graph), virtual
Fig. 3. E-RED in a large capacity network: real queue length (top graph),                                                                                                              queue length (middle graph), and throughput (bottom graph).
virtual queue length (middle graph), and throughput (bottom graph).

                              TABLE I                                                                                                  Virtual Queue Length (packets) Vs Time (seconds): alpha=0.001


                                                                           virtual queue length (packets)
 AVQ/Scenario    Avg(qlen) (pkts)   Std(qlen) (pkts)   Avg(thr) (Mbps)                                      40000

 RED/LC          117                131                939
 E-RED/LC        9.74               11.8               951(≈ γc)                                            30000

 RED/LD          27.7               34.3               278                                                  20000

 E-RED/LD        11.8               16.2               285(≈ γc)

                                                                                                                       0        10            20        30          40          50         60           70   80
                                                                                                                                                              time (seconds)

mainly due to the use of a virtual queue. We have performed                                                 45000
                                                                                                                                       Virtual Queue Length (packets) Vs Time (seconds): alpha=0.001

simulations with RED in a virtual queue (REDvq) and found                                                   40000

that REDvq is unstable if the virtual queue limit and the REDvq                                             35000

                                                                           virtual queue length (packets)
parameters are taken to be the same as those of RED in the real                                             30000

queue. The reason for the instability is that the link adaptation                                           25000

speed is much larger than what Theorem III.2 suggests. From                                                 15000

Fig. 3, we see that at equilibrium, bl ≈ 27000 packets. Since                                               10000

we have chosen ξ = 1, we get                                                                                 5000


                         2(27000 − 60) × 8320
                                                                                                                       0                      50                   100                     150               200
                                                                                                                                                              time (seconds)

     pl = 0.0005 exp(                         ) = 0.0442 ,
                               0.1 × 109                                 Fig. 6. The virtual queue of E-RED-aq in large capacity (top graph) and large
                                                                         delay networks (bottom graph): αl = 0.001.
and the following stability condition on link adaptation speed:

                      2 × 0.0442 × 8320
         gl (ˆl ) ≤
             p                          = 7.36 × 10−6                    C. Sudden Traffic Changes
                          0.1 × 109
                                                                            To study the transient response of the proposed algorithm,
For RED, the default setting for pmax is 0.1 and this requires           we consider a scenario where a large traffic source suddenly
                                                                         joins the network and leaves after a while. We choose c =
      maxthresh − thresh ≥                      = 13584 ,                100Mbps and N = 200, and the round trip delays to be from
                                    7.36 × 10−6                          20ms to 100ms. One UDP flow, with fixed rate of c/3 joins at
and hence a much larger virtual queue length. If we increase             50 and leaves at 70. The virtual queue is shown in Fig. 7. We
the virtual queue limit 120 fold so that the difference between          see that the transient response is fast.
the two thresholds is now 14400 (which is larger than 13584),
                                                                                                                                     Virtual Queue Length (packets) Vs Time (seconds): UDP In and Out

then the simulations (not shown here due to space limitations)                                              7000

indicate that the system is stable under these new parameter                                                6000

settings. So both linear and exponential marking profiles can                                                5000
                                                                           virtual queue length (packets)

stabilize the system if the adaptation speed is chosen according                                            4000

to the condition in Theorem III.2. However, for linear profile

to satisfy the stability condition, we need the knowledge of
pl , while E-RED does not require that. This auto-configuration                                              2000

is one of the reasons for choosing the exponential marking                                                  1000

profile.                                                                                                       0
                                                                                                                   0       10            20        30         40         50          60       70        80   90
                                                                                                                                                              time (seconds)

Remark 4. From the above simulation results, we see that
once the virtual queue becomes stable (oscillates around its             Fig. 7. UDP traffic in and out, virtual queue
equilibrium point slightly), the system will become stable, and
the real queue size will be small and the throughput will
oscillate around γl cl slightly. In the following simulations, to        D. Dependence of Stability on the Parameters: ξ, γ and pmin,l .
save space, we provide the graphs of only the virtual queues
for the E-RED algorithm.                                                    We then study the influence of the parameters on stability
                                                                         and performance. We have shown that ξ = 1 also stabilizes the
                                                                         system while ξ ≤ 1/2 is suggested in Theorem III.2. To further
B. E-RED-iq and E-RED-aq                                                 test how conservative the condition is, we have chosen ξ to be
                                                                         2, 4 and 8. The network parameters are chosen as c = 200
   We then test the stability and performance of E-RED-aq. We            Mbps, N = 200, and RT T between 20 and 100 ms. The E-
still choose the topologies as in the previous subsection, and           RED parameters are chosen as pmin,l = 0.0005, pmax,l = 0.1,
choose αl = 0.001 and ξ = 0.9. The virtual queue size of the             γ = 0.9. We have plotted the virtual queue lengths for the
two network scenarios are shown in Fig. 6. From the graphs,              three ξ values in Fig. 8. We see that as ξ increases, the virtual
we see that E-RED-aq is also stable with TCP-Reno.                       queue shows higher oscillation, and when ξ = 8, the system is

unstable. So this simulation shows that the slow link adaptation                                                       E. TCP-Reno, HighSpeed-TCP, and Scalable-TCP
condition is not very conservative.                                                                                       We studied the combination of E-RED with two pop-
                                                                                                                       ular TCP variants for high-speed networks: HighSpeed-
                                                  Virtual Queue Length (packets) Vs Time (seconds): beta=2
                                                                                                                       TCP [8] and Scalable-TCP [18]. We performed ns-2 simulation
                                                                                                                       for HighSpeed-TCP, and performed Matlab simulation for

                                                                                                                       Scalable-TCP since it is not included in the current ns-2
                                                                                                                       package. For both simulations, the scenario is the following:
  virtual queue length (packets)


                                                                                                                       N = 3, the capacity per user is 10 Mbps, and the round trip
                                                                                                                       delay for each user is 200ms. The window behavior of the

                                                                                                                       three users under HighSpeed TCP is shown in Fig. 9. The
                                   1000                                                                                average throughput per user measured over 75 seconds is 0.8c.
                                    500                                                                                For a small number of sources, the use of fluid models is
                                          0   5     10          15          20
                                                                      time (seconds)
                                                                                        25          30       35   40   questionable since there are not enough sources for the law of
                                                  Virtual Queue Length (packets) Vs Time (seconds): beta=4             large numbers to hold [31]. Despite this, the combination of E-
                                                                                                                       RED with HighSpeed TCP gives a reasonably high throughput.
                                                                                                                       The fact that the fluid model does not apply was noticed
                                                                                                                       in the simulations from the behavior of the queue length.
  virtual queue length (packets)


                                                                                                                       The queue length does not converge to its equilibrium value.
                                                                                                                       However, simulations (not shown here) have indicated that if
                                                                                                                       N is sufficiently large, the system converges to its equilibrium
                                    500                                                                                under E-RED.
                                      0                                                                                                                             Window Size (packets) Vs Time (seconds)
                                          0   5     10          15          20          25          30       35   40
                                                                      time (seconds)                                                                                                                          window size 1
                                                                                                                                                                                                              window size 2
                                                  Virtual Queue Length (packets) Vs Time (seconds): beta=8                                      450                                                           window size 3

                                                                                                                        window size (packets)

  virtual queue length (packets)


                                    600                                                                                                         150


                                    200                                                                                                          0
                                                                                                                                                      0   10   20           30            40         50         60            70
                                                                                                                                                                                 time (seconds)
                                          0   5     10          15          20          25          30       35   40
                                                                      time (seconds)

                                                                                                                       Fig. 9. The window sizes of the 3 HS-TCP flows with E-RED.
Fig. 8. The virtual queue length of E-RED, ξ = 2, 4, 8 (in top, middle and
bottom graphs respectively).
                                                                                                                          The Matlab simulation for Scalable-TCP shows that if ξ = 1,
                                                                                                                       both the source rates (shown in Fig. 10) and the link marking
   In simulations not shown here, we have also studied the                                                             probability (not shown here) oscillate, without a trend of either
influence of γ on the system performance. We studied two                                                                divergence or convergence. If ξ = 0.9, all rates and probability
scenarios, one with c = 150 Mbps and N = 100, and another                                                              converge. These results suggest that ξ = 1 is the critical point
one with the same c but N = 250. For the first scenario, we                                                             between stability and instability. This Matlab simulation also
have seen that the system is unstable for γ > 0.93, and is                                                             supports our earlier claim that the slow adaptation condition
stable for γ ≤ 0.93. For the second scenario, we have seen                                                             in Theorem III.2 is not only sufficient but also very close to
that the system is unstable for γ > 0.97, and is stable for                                                            being necessary.
γ ≤ 0.97. For both scenarios, once γ is small enough for the
system to be stable, the oscillation level does not change much                                                        F. Multi-link Network Simulations
as γ decreases. Thus we know that for a fixed network scenario,                                                            All the simulations above were for a single-link network.
there is a threshold value of γ, under which the system is stable.                                                     Our theoretical analysis applies to a general topology network
This threshold value can be larger if the number of sources is                                                         and in this subsection we will examine the performance of E-
increased while keeping the per flow throughput constant. This                                                          RED in a multi-link network. The network is composed of 6
is to be expected since what we have observed is essentially a                                                         routers and 500 source-destination pairs (users), as shown in
statistical multiplexing effect.                                                                                       Fig. 11. The 500 flows from the 500 users are all FTP traffic
   In simulations not shown here, we have also seen that                                                               and they go along 5 different paths: R1 − R2 − R4 − R5 (L1),
if pmin,l is smaller than the equilibrium value p, the value                                                           R3 − R2 − R4 − R6 (L2), R1 − R2 − R3 (L3), R6 − R4 − R5
of pmin,l only changes the initial setup time, and does not                                                            (L4), and R6 − R4 − R2 − R3 (L5), with each path containing
influence the stability and transient response. However, if                                                             100 flows. All the inter-router links have a capacity of 100
pmin,l > p, we will never reach the operating point and the                                                            Mbps and a delay of 10 ms; and all the user-router links have
system cannot be stable.                                                                                               a capacity of 10 Mbps (large enough for these links not to be

                                           scalable TCP: rates
                                                                                                                                                                  Virtual Queue Length (packets) Vs Time (seconds)



                                                                                                     virtual queue length (packets)




               0    20    40   60        80       100       120   140    160      180        200                                             0            5       10          15              20          25     30   35   40
                                                 time t                                                                                                                                 time (seconds)
                                           scalable TCP: rates
                                                                                                                                                                            Throughput (bps) Vs Time (seconds)



                                                                                                     Throughput (bps)




               0    20    40   60        80       100       120   140    160      180        200                                                     0        5        10          15          20          25    30   35   40
                                                 time t                                                                                                                                  time (seconds)

                                                                                                   Fig. 12. The virtual queue length (top graph) and throughput (bottom graph)
Fig. 10. Source rates for Scalable TCP+E-RED: ξ = 1 (top graph) and ξ = 0.9                        of the link between R2 and R4 in a multilink network.
(bottom graph).

                                                                                                                        •              The choice of pmin,l does not influence the stability and
bottlenecked) and a delay uniformly distributed between 0 ms                                                                                                                   ˆ
                                                                                                                                       transient response, given that pmin,l < p,
and 10 ms. We plot the virtual queue length and throughput of                                                           •              If ξ > 1/2, the system might be unstable. If ξ ≤ 1/2 and
the link between R2 and R4 in Fig. 12; those of the other inter-                                                                       the system is unstable because N is not large enough, the
router links show similar behavior. From the graphs, we see                                                                            system cannot be stabilized by making ξ even smaller.
that E-RED stabilizes TCP-Reno for this multi-link multi-path                                                                          This suggests that the large oscillations for small N
multi-flow network and achieves satisfactory performance.                                                                               are due to the TCP’s AIMD behavior and cannot be
                                                                                                                                       eliminated through slow adaptation speed.
         R1                                                                             R5
                                                                                                                                                                        VI. C ONCLUSION
                                    path 1: flow 1-100                       L4                       In this paper, we have introduced a general class of primal-
          path 3:                                                                  path 4:
          flow                             L3                                      flow            dual algorithms and the E-RED algorithm. This class of primal-
          201-           R2                                             R4
                                                                                                   dual algorithms has been shown to be locally stable in the
                                     path 2: flow 101-200
                                                                                                   presence of delay. The E-RED algorithm has been shown to
                    L2              path 5: flow 401-500
                                                                                                   stabilize TCP-Reno and its high speed variants, both analyt-
                                                                                                   ically as well as through simulations. The simulations were
                                                                                                   performed under different scenarios and different parameter
Fig. 11. The multilink network topology.                                                           choices. These simulations clearly show that E-RED outper-
                                                                                                   forms RED with larger throughput and lower queueing delay.
                                                                                                   They also suggest guidelines for tuning the E-RED parameters.
G. Summary of the Simulations                                                                                              VII. A PPENDIX
    We summarize here our results from the simulations:                                               In this appendix we provide proofs for Theorems III.2, IV.1,
    • For the general network with heterogeneous delays, E-                                        and IV.2. First, we present the following lemmas as preliminary
      RED stabilizes TCP-Reno, even when the capacity or the                                       to the proof of Theorem III.2 and Theorem IV.1:
      round trip delays are very large, given the condition that
      the number of flows is sufficiently large.                                                     Lemma VII.1. If θ1 > 0, θ2 > 0, T1 > 0, T2 > 0, Tm =
    • E-RED-aq also stabilizes TCP-Reno, by reducing the link
                                                                                                   max(T1 , T2 ), λ1 ≥ 0, λ2 ≥ 0, λ1 + λ2 ≤ 1, then
      adaptation speed (Theorem IV.1).                                                                                                                   T1    θ1 e−jωT1           T2    θ2 e−jωT2
    • The transient response is fast when there are sudden
                                                                                                   f (ω) = λ1                                                                 + λ2
                                                                                                                                                         Tm jωT1 (jωT1 + θ1 )      Tm jωT2 (jωT2 + θ2 )
      changes in the available bandwidth.
                                                                                                   crosses the real line at the right of −2 as ω varies from −∞
    • For a fixed network scenario, there is a threshold value of
                                                                                                   to ∞.
      γ to guarantee stability, and for most realistic scenarios,
      this value of γ could be between 0.9 and 0.95. As N                                          Proof. It is easy to see that f (−ω) = f ∗ (ω), where f ∗ (ω) is
      increases, this value of γ can be made very close to 1.0.                                    the convex conjugate of f (ω), so we only need to consider the

case of ω > 0. Without loss of generality, we assume T1 ≤ T2 ,                 If φ2 + x2 > φ1 , then φ2 > φ1 − x2 > 0, since φ1 ≥ π/2 −
then Tm = T2 . Let x1 = ωT1 , x2 = ωT2 , then x2 ≥ x1 > 0,                  1/2 > 1/2 ≥ x2 . So cos φ2 < cos(φ1 − x2 ) = cos φ1 cos x2 +
     T1        T2
and Tm = x1 , Tm = 1. Define
           x2                                                               sin φ1 sin x2 and thus we have
                        θi e−jxi       1                                     |R|    =   x2 [λ1 cos φ1 sin(φ1 + x1 ) + λ2 cos φ2 sin(φ2 + x2 )]
       Ci (xi ) :=                   =    cos φi e−jxi −φi −π/2 ,                   ≤   1
                     jxi (jxi + θi )   xi                                               x2 [λ1 cos φ1 sin(φ1 + x1 )
                                                                                        +λ2 (cos φ1 cos x2 + sin φ1 sin x2 ) sin(φ2 + x2 )]
where φi = arctan(xi /θi ) ∈ (0, π/2). Then, f (ω) =                                    1
                                                                                    ≤   x2 [λ1 cos φ1 + λ2 (cos φ1 + sin x2 )]
λ1 x2 C1 (x1 ) + λ2 C2 (x2 ). Let R := Re(f (ω)) and I :=                           <   1
                                                                                        x2 [λ1 x2 + λ2 (x2 + x2 )] ≤ 2 .
Im(f (ω)). Then,
                                                                               So under case 2, we still have R > −2. Then, we have
      λ1 cos φ1 sin(φ1 + x1 ) + λ2 cos φ2 sin(φ2 + x2 )                     proved that R > −2 if I = 0 and thus the curve crosses the
  R=−                                                   ,
                             x2                                             real line to the right of −2.
and                                                                         Lemma VII.2. If λi ≥ 0, θi > 0, Ti > 0, ∀i = 1, 2, 3 and
      λ1 cos φ1 cos(φ1 + x1 ) + λ2 cos φ2 cos(φ2 + x2 )                     Tm = max(T1 , T2 , T3 ), λ1 + λ2 + λ3 = 1, then
  I=−                                                   .
                             x2                                                               3                   3
                                                                                                                            Ti    θi e−jωTi
                                                                                   f (ω) =         λi gi (ω) =         λi
  To show that the curve crosses the real line to the right of                                                              Tm jωTi (jωTi + θi )
                                                                                             i=1                 i=1
−2, it is sufficient to show that R > −2 if I = 0. Since
R > −2 if x2 > 1/2, we only need to consider the case of                    crosses the real line to the right of −2 as ω varies from −∞
x1 ≤ x2 ≤ 1/2. From I = 0, we get                                           to ∞.
                                                                            Proof. Supppose f (ω) crosses the real line to the left of
       λ1 cos φ1 cos(φ1 + x1 ) = λ2 cos φ2 (− cos(φ2 + x2 ))
                                                                            −2; then there exists an ω ∗ such that Im(f (ω ∗ )) = 0 and
We consider the following two cases:                                        Re(f (ω ∗ )) ≤ −2. If s :=          i=1 λi < 1, then we can pick
   Case 1: cos(φ1 + x1 ) > 0 and cos(φ2 + x2 ) < 0, which                   λi = λi /s, and the new combination f (ω ∗ ) = i=1 λi gi (ω ∗ )
lead to π > φ2 + x2 > π/2 > φ1 + x1 > 0. Then, we have                      also lies on the real line to the left of −2. So we only
cos φ2 = sin(π/2 − φ2 ) < sin x2 < x2 , and 0 < − cos(φ2 +                  need to consider the case when           i=1 λi = 1, under which
x2 ) = sin(φ2 + x2 − π/2) < sin x2 < x2 .                                   f (ω ) lies in the triangle formed by A1 , A2 and A3 , where
   If φ1 + x1 ≤ φ2 , then cos(φ1 + x1 ) ≥ cos φ2 . So we have               Ai , i = 1, 2, 3 are the points in the complex plan corresponding
                                                                            to gi (ω ∗ ), i = 1, 2, 3. From the triangle property, there exists
                   λ2 − cos φ2 cos(φ2 +x2 ) sin(φ1 +x1 )                    some point on one of the three edges of the triangle that is on
      |R|    =     x2 [         cos(φ1 +x1 )
                   + cos φ2 sin(φ2 + x2 )]                                  the real line to the left of −2. From Lemma VII.1, we know
             ≤     λ2                                                       that this cannot be true.
                   x2 [− cos(φ2 + x2 ) sin(φ1 + x1 )
                   + cos φ2 sin(φ2 + x2 )]                                  Lemma VII.3. For any integer N > 0, if λi ≥ 0, θi >
                   1                                          x2 +x2
             ≤     x2 [(− cos(φ2 + x2 )) + cos φ2 ] <           x2     =2   0, Ti > 0, ∀i = 1, 2, ..., N and Tm = max(Ti : i =
                                                                            1, 2, ..., N ), i=1 λi ≤ 1, then
  If φ1 + x1 > φ2 , then φ1 > φ2 − x1 ≥ φ2 − x2 > 0, since
φ2 > π/2 − 1/2 > 1/2 ≥ x2 . So cos φ1 < cos(φ2 − x2 ) =                                       N                  N
                                                                                                                            Ti    θi e−jωTi
cos φ2 cos x2 + sin φ2 sin x2 and thus we have                                     f (ω) =         λi gi (ω) =         λi
                                                                                             i=1                 i=1
                                                                                                                            Tm jωTi (jωTi + θi )
 |R|     =       x2 [λ1 cos φ1 sin(φ1 + x1 ) + λ2 cos φ2 sin(φ2 + x2 )]
                 1                                                          crosses the real line at a point to the right of −2 as ω varies
         ≤       x2 [λ1 (cos φ2 cos x2 + sin φ2 sin x2 ) sin(φ1 + x1 )      from −∞ to ∞.
             +λ2 cos φ2 sin(φ2 + x2 )]
             1                                                              Proof. From Caratheodory’s Theorem ([3], Proposition B.6,
         ≤   x2 [λ1 (cos φ2 + sin x2 ) + λ2 cos φ2 ]
         <   1                                                              Page 679), let X be a subset of Rn ; then every element of
             x2 [λ1 (x2 + x2 ) + λ2 x2 ] ≤ 2 .
                                                                            conv(X) can be represented as a convex combination of no
   So under case 1, we have R > −2.                                         more than n + 1 elements of X. So the convex combination of
   Case 2: cos(φ1 + x1 ) ≤ 0 and cos(φ2 + x2 ) ≥ 0, which                   gi (ω)’s can be represented as a convex combination of three
lead to π > φ1 + x1 ≥ π/2 ≥ φ2 + x2 > 0. Then, we have                      gi (ω)’s. From Lemma VII.2, we know that it is true for any
cos φ1 = sin(π/2 − φ1 ) ≤ sin x1 < x1 < x2 , and − cos(φ1 +                 integer N .
x1 ) = sin(φ1 + x1 − π/2) < sin x1 < x1 ≤ x2 .
   If φ2 + x2 ≤ φ1 , then cos(φ2 + x2 ) ≥ cos φ1 . So we have                  With Lemma VII.3 at hand, we can now prove Theorem III.2
                                                                            and Theorem IV.1:
                   λ1                                                                                         ˜        q x        x
                                                                            Proof of Theorem III.2. Let ar := −ˆr /(ˆr Ur (ˆr )). Then,
      |R|    =     x2 [cos φ1 sin(φ1 + x1 )
                   + cos φ1 (− cos(φ1 +x1 )))sin(φ2 +x2 ) ]                             ˜
                                                                            from (29), ar ≤ ar .
                                 cos(φ2 +x2
                   λ1                                                                         p       p
                                                                               Let βl := (gl (ˆl )cl /ˆl ). Then from (28) and (29), we have
             ≤     x2 [cos φ1 sin(φ1 + x1 )
                   +(− cos(φ1 + x1 )) sin(φ2 + x2 )]                                         Tr a r x r
                                                                                                ˜ ˆ     θr e−sTr                  ˆ
                                                                                                                               βl p l
             ≤     1                                          x2 +x2        L(s) = diag                            AT (−s)diag        A(s) .
                   x2 [cos φ1 + (− cos(φ1 + x1 ))] <            x2     =2                       ˆ
                                                                                                qr sTr (sTr + θr )              cl

Since the open-loop system is stable, by the generalized                     where
Nyquist Criterion [5] the closed-loop system is asymptotically
stable if the eigenloci of L(jω) do not encircle −1. Now the                  ˆ                            ˆ
                                                                                                        βl p l                2Tm ar xr jω + κr
                                                                                                                                  ˜ ˆ
                                                                              A(jω) = diag                     A(jω)diag                        .
eigenvalues of L(jω) are identical with those of                                                         cl                      ˆ
                                                                                                                                 qr       κr

               Tr     θr e−jωTr                                                Given the condition in (30), we have:
  L(jω) = diag                       ˆ       ˆ
                                     AT (−jω)A(jω) ,
               Tm 2jωTr (jωTr + θr )                                                       ˆ            ˆ       ˆ
                                                                                      σ 2 (A(jω)) = ρ(AT (−jω)A(jω))
where                                                                                          a      ˆ
                                                                                              2˜r Tm xr T                  ˆ
                                                                                                                       βl p l    κl
                                                                                 =    ρ(diag(           )A (−jω)diag(                   )A(jω))
                                                                                                  qr                      cl |jω + κl |
                              βl p l
                                 ˆ                          ˜ ˆ
                                                        2Tm ar xr                            2ar Tm T                         κl
        A(jω) = diag                 A(jω)diag                    .              ≤     diag(         )A (−jω)diag(βl pl |
                                                                                                                     ˆ            |)
                               cl                          ˆ
                                                           qr                                   ˆ
                                                                                               qr                         jω + κl
                                                                                      × diag( yl )A(jω)diag(ˆr ) ≤ 1 × 1 = 1 .
                                                                                               ˆ            x
  Let σ(Z) denote the largest singular value of a square matrix
Z and ρ(Z) its spectral radius. Given the condition in (30), we              The last inequality above uses (47) and
have:                                                                                            κl               1
                                                                                        βl |           | ≤ βl ≤        ,       ∀r, l : l ∈ r ,
              ˆ  2                ˆT
          σ (A(jω)) = ρ(A (−jω)A(jω))                                                          jω + κl          2ar Tm
                   a      ˆ
                  2˜r Tm xr T                 ˆ
                                           βl p l                            and thus the absolute row sums of these matrices are all less
        = ρ(diag(           )A (−jω)diag(         )A(jω))
                      qr                     cl                              than or equal to 1.
                2ar Tm T                                                        Now, if λ is an eigenvalue of L(jω), then
        ≤ diag(                          ˆ
                         )A (−jω)diag(βl pl )
                   1                                                                                               θr e−jωTr
          × diag( )A(jω)diag(ˆr )                                                              λ ∈ Co(0 ∪ {                       }) .
                   ˆ                                                                                           2jωTm (jωTr + θr )
        ≤ 1 × 1 = 1.
                                                                             From Lemma VII.3, the eigenloci of L(jω) do not encircle -1;
  The last inequality above uses the facts that                              thus by the generalized Nyquist Criterion [5], the closed-loop
                                                                             system is stable.
              ˆ    ˆ
              xr = yl = cl , ∀l ∈ L ,           ˆ    ˆ
                                                pl = qr , ∀r ∈ R ,    (47)     We finally prove Theorem IV.2.
      r:l∈r                             l:l∈r                                Proof of Theorem IV.2. Note that:
and                                                                                                pl (1 − pl )     ≤ qr (1 − qr )
                                1                                                            l∈r
                       βl <          , ∀r : l ∈ r ,                                     ⇐⇒                 zl
                                                                                                                    ≤ e vr − 1
                              2ar Tm                                                                 l∈r (e − 1)
                                                                                        ⇐⇒                a
                                                                                                     l∈r (˜l − 1)   ≤        ˜
                                                                                                                         l∈r al − 1
and thus the absolute row sums of these matrices are all less                           ⇐⇒
                                                                                                          ai        ≤
                                                                                                     i=1                 i=1 ai + N − 1 ,
than or equal to 1.
   Now, if λ is an eigenvalue of L(jω), then                                 where ai := ezl ≥ 1 for all l, and (ai , 1 ≤ i ≤ N ) is a
                                                                             permutation of (˜l , l ∈ r). Let us now prove the last inequality
                               Tr    θr e−jωTr                               by induction: it is obviously true for N = 1 and N = 2. Let
               λ ∈ Co(0 ∪ {                         }) .
                              2Tm jωTr (jωTr + θr )                          us suppose that it is true for N . Then, for N + 1, we have
From Lemma VII.3, the eigenloci of L(jω) do not encircle -1;                     N +1
                                                                                        ai     ≤
                                                                                 i=1                   i=1 ai + N − 1 + aN +1
thus by the generalized Nyquist Criterion [5], the closed-loop                                 =
                                                                                                       N +1       N
                                                                                                       i=1 ai +   i=1 ai (1 − aN +1 )
system is stable.                                                                                     +N + (aN +1 − 1)
                                                                                                       N +1              N
                                               θr e−jωTr                                       =       i=1 ai + N − ( i=1 ai − 1)(aN +1           − 1)
Remark 5. We can also show that                                                                        N +1
                                          jωTr (jωTr + θr )                                    ≤       i=1 ai + ((N + 1) − 1)
always crosses the real axis to the left of the point −2/π,
which suggests that our local stability condition will not be                Thus the inequality also holds for N + 1, and therefore for all
too conservative regardless of the value θ takes in [0, ∞).                  N.

Proof of Theorem IV.1. Define ar as in the proof of Theo-
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                                                                                                            Tamer Basar (S’71-M’73-SM’79-F’83) received
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[19] S. Kunniyur and R. Srikant, “End-to-end congestion control: utility                                    M.S., M.Phil, and Ph.D. degrees in engineering and
     functions, random losses and ECN marks,” in Proc. IEEE Infocom, Tel                                    applied science from Yale University. After stints
     Aviv, Israel, March 2000.                                                                              at Harvard University and Marmara Research Insti-
[20] S. Kunniyur and R. Srikant, “Analysis and design of an adaptive virtual                                tute (Gebze, Turkey), he joined the University of
     queue algorithm for active queue management,” in Proc. ACM Sigcomm,                                    Illinois at Urbana-Champaign in 1981, where he is
     pages 123–134, San Diego, CA, Aug. 2001.                                                               currently the Fredric G. and Elizabeth H. Nearing
                                                                                                            Professor of Electrical and Computer Engineering.
[21] S. Kunniyur and R. Srikant, “Designing AVQ parameters for a general
                                                                                                            He has published extensively in systems, control,
     topology network,” in Proc. Asian Control Conf., Singapore, Sept. 2002.
                                                                                                            communications, and dynamic games, and has cur-
[22] S. Kunniyur and R. Srikant, “Note on the stability of the AVQ scheme,”
                                                                                   rent interests in modeling and control of communication networks, control
     in Proc. Conf. Information Sciences and Systems, Princeton, NJ, March
                                                                                   over heterogeneous networks, resource management and pricing in networks,
                                                                                   and robust identification and control.
[23] S. Kunniyur and R. Srikant, “A time-scale decomposition approach to                     ¸                                                             a
                                                                                      Dr. Basar is the Editor-in-Chief of Automatica, Editor of the Birkh¨ user
     adaptive ECN marking” IEEE Trans. Automatic Control, 47(6):882–894,           Series on Systems & Control, Managing Editor of the Annals of the Inter-
     June 2002.                                                                    national Society of Dynamic Games (ISDG), and member of editorial and
[24] S. Liu, T. Basar and R. Srikant, “Controlling the Internet: a survey and      advisory boards of several international journals. He has received several
     some new results,” in Proc. IEEE Conf. Decision and Control, Maui,            awards and recognitions over the years, among which are the Medal of Science
     HI, Dec. 2003.                                                                of Turkey (1993), Distinguished Member Award (1993), Axelby Outstanding
[25] S. H. Low and D. E. Lapsley, “Optimization flow control, I: Basic              Paper Award (1995) and Bode Lecture Prize (2004) of the IEEE Control
     algorithm and convergence,” IEEE/ACM Trans. Networking, pages 861–            Systems Society (CSS), and the Quazza Medal (2005) of IFAC. He is a member
     875, Dec. 1999.                                                               of the National Academy of Engineering, a member of the European Academy
[26] S. Low, F. Paganini, J. Wang, S. Adlakha and J. Doyle, “Dynamics of           of Sciences, a Fellow of IEEE, a past president of CSS, and a past (founding)
     TCP/RED and a scalable control,” in Proc. IEEE Infocom, June 2002.            president of ISDG. His email address is: tbasar@control.csl.uiuc.edu.
[27] V. Misra, W. Gong, and D. Towsley, “A fluid-based analysis of a network
     of AQM routers supporting TCP flows with an application to RED,” in
     Proc. ACM Sigcomm, Stockholm, Sweden, Sept. 2000.
[28] J. Mo and J. Walrand, “Fair end-to-end window-based congestion
     control,” IEEE/ACM Trans. Netowrking, 8(5):556–567, Oct. 2000.
[29] F. Paganini, J. Doyle, and S. Low, “Scalable laws for stable network
     congestion control,” in Proc. IEEE Conf. Decision and Control, Dec.                                      R. Srikant (S’90-M’91-SM’01) received his B.Tech.
     2001.                                                                                                    from the Indian Institute of Technology, Madras in
[30] F. Paganini, Z. Wang, J. Doyle, and S. Low, “A new TCP/AQM for stable                                    1985, his M.S. and Ph.D. from the University of Illi-
     operation in fast networks,” in Proc. IEEE Infocom, San Francisco, CA,                                   nois in 1988 and 1991, respectively, all in Electrical
     April 2003.                                                                                              Engineering. He was a Member of Technical Staff
                                                                                                              at AT&T Bell Laboratories from 1991 to 1995. He
[31] R. Srikant, The Mathematics of Internet Congestion Control. Birkh¨ user,
                                                                                                              is currently with the University of Illinois, where he
     Boston, 2004.
                                                                                                              is a Professor in the Department of Electrical and
[32] G. Vinnicombe, “On the stability of end-to-end congestion control
                                                                                                              Computer Engineering and a Research Professor in
     for the Internet,” University of Cambridge Technical Report CUED/F-
                                                                                                              the Coordinated Science Lab.
     INFENG/TR.398, 2001. Available at http://www.eng.cam.ac.uk/˜gv.
                                                                                                                 He was an associate editor of Automatica, and is
[33] G. Vinnicombe, “On the stability of networks operating TCP-like               currently on the editorial boards of the IEEE/ACM Transactions on Networking
     congestion control,” in Proc. IFAC World Congress, Barcelona, Spain,          and IEEE Transactions on Automatic Control. He was the chair of the 2002
     2002. Available at http://www.eng.cam.ac.uk/˜gv.                              IEEE Computer Communications Workshop and will be a program co-chair
[34] G. Vinnicombe,         “Robust congestion control for the Internet,”          of IEEE INFOCOM in 2007. His research interests include communication
     University of Cambridge Technical Report, 2002. Available at                  networks, stochastic processes, queueing theory, information theory, and game
     http://www.eng.cam.ac.uk/˜gv.                                                 theory. His email address is: rsrikant@uiuc.edu.
[35] J.T. Wen and M. Arcak, “A unifying passivity framework for network
     flow control,” IEEE Trans. Automatic Control, 49(2):162–174, 2004.

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