Docstoc

Comparison of Q Routing and Shortest Path Algorithms

Document Sample
Comparison of Q Routing and Shortest Path Algorithms Powered By Docstoc
					Comparison of Q Routing and
  Shortest Path Algorithms

           Firat Tekiner, Z Ghassemlooy
     Optical Communications Research Group
          University of Northumbria, UK

          Srikanth Thadigol Reddappa
         Sheffield Hallam University, UK
    overview

   Routing Algorithms

        Shortest Path Routing Algorithm
        Q Routing
        Dual Reinforcement Q Routing

   NSFNET
   PVM
   Simulation Results
   Conclusions
    Routing
   Routing is the transmission of data (packets) from a source s to
    its destination d on the network or internetwork.




   At every node the packet is received, stored and then routed to
    the next hop until it reaches its destination
 Routing Algorithms

Path Determination
 Computes the optimal path to every node in the network based
  on the routing metric of the links.




Switching
 Forwards the packet to the next hop of the optimal path until it
  reaches destination
    Classification
   Static Versus Dynamic
   Single-Path Versus Multipath
   Flat Versus Hierarchical
   Host-Intelligent Versus Router-Intelligent
   Intradomain Versus Interdomain
   Link-State Versus Distance Vector
   Adaptive Versus Non-Adaptive

         Adaptive Eg. Shortest Path Routing

         Non-Adaptive Eg. Q-learning methods, hybrid agent
         based Distance Vector algorithms
Shortest Path Algorithm
               Let
                    V : the array of all the edges (nodes)
                    D[n]: the Array of Shortest cost of the
                     vertices(links)
                    C[s,w]: the cost of the vertices
                    s: Router Id
                    w: the neighbour

                     Initialisation
                      D[w] = ∞
                     w ->(V-s) with minimal C[s, w]
                     Relax the node
                     D[v]= min(D[v], D[w]+c[w,v]),
                     where C[w,v] are the costs of the vertices,
                     connected to w
                     Greedy Algorithm- Optimises the routes to
                     all the nodes to a single least cost path and it
                     insists to take this single best path without
                     regard to future consequences.
    Shortest Path Routing
   Computes Shortest Path using the Dijkstra Algorithm.

   Routes the packets based on least cost of the links

   Non-adaptive Routing Algorithm - routing table never change once initial routes
    have been selected unless there is a route failure

Drawbacks

   Sometimes there may be congested queues in the intermediate nodes and
    packets spend waiting in the queues, which delays the packets travelling to the
    destination.

   For node r lying in the shortest path from s to d, it is also shortest path to r to s
    and d and vice versa
    Q Routing
   Reinforcement learning – all the nodes in the network learn a global
    routing policy

   Builds a routing table based on the delivery times (Q values) of the
    packets to every node in the network.

   Routes the packet to the destination on path with least delivery time

   For every data packet routed to the next hop, the node gets back a
    learning update about the remaining delivery time estimate for the packet
    to reach destination (Forward Exploration)

   Synchronised Q Information at every node in the network – balances
    minimizing the number of “hops” a packet will take with the possibility of
    congestion along popular routes.
Forward Exploration
                     When a node x sends a packet to
                      node d via its neighbour y, it gets
                      back y’s estimated remaining trip
                      times to the destination d, selects
                      the neighbour with the smallest
                      delivery time


                        Q x ( z , s )  min Q y ( z , s )
                                        z N ( y )


                    Qy ( y, s)  b (Qx ( z, s)  q y  Qy ( x, s))

                       ηb is the learning rate parameter
    Dual Reinforcement Q Routing

   Modified Q Routing with backward exploration along with
    backward exploration – 2 way learning

   Every data packet carries the Q Information- the delivery time
    estimate of the sender node to its processor node (Backward
    Exploration)

   However adds overload on the packet
Backward Exploration
                  When x sends a packet to node y to
                   gets its estimated remaining trip times,
                   y gets x’s estimated trip times for its link
                   with s.


                       Q x ( z , s )  min Q y ( z , s )
                                      z N ( y )

               Qy ( y, s)  b (Qx ( z, s)  q y  Qy ( x, s))

                   ηb is the learning rate parameter
NSFNET
    PVM

   Software used for distributed computation

   Permits a heterogeneous collection of Unix and/or Windows
    computers hooked together by a network to be used as a single
    large parallel computer.

   Supports C, C++ , and Fortran languages

   Built in library functions

   Identifies the application as multiple tasks
Simulation

Routing algorithms were implemented using PVM in C language on
LINUX platform.

Master and slave paradigm: Every node of the NFSNET was
assigned to a PVM task or a slave process, which is spawned by an
initialising task or a master process on the same machine.
                                                   slave process
                  Spawns
 Master Process




Single-program multiple-data Model
Tasks with similar function executed in parallel
Simulation Conditions

    NSFNET is used as the network topology

    Random uniform traffic distribution is used with varying packet creation
     rates.

    For each simulation, packet generation is stopped after creating 5000
     packets per node and simulation is stopped after all packets are arrived
     to their destinations or detected and deleted from the network.

    A random link fails every 5 seconds for a period of 4 seconds and
     traffic is directed to other neighbours.

    Hello packets were exchanged between the nodes to know whether its
     neighbouring nodes are active.

    Payload is fixed –512 Kbps
    Performance Parameters


    Average packet delay:
     Is the average delay a packet experiences while being routed
     from source to destination.

    Average throughput per packet:
     Is the average number of packets being forwarded by a node
     for the duration of the simulation.
Average Packet Delay Vs. System Load

                               1.25        OSPF
                                           QR
                               1.05        DRQR
      Avg. Packet Delay(sec)




                               0.85


                               0.65


                               0.45


                               0.25


                               0.05
                                  0.025   0.075       0.125       0.175      0.225   0.275
                                                  Load - Packet Creation Rate(sec)
Throughput Vs. System Load
                            300
                                        OSPF
                                        QR
                            250         DRQR
    Throughput(kbits/sec)




                            200



                            150



                            100



                             50
                              0.025   0.075    0.125     0.175     0.225     0.275
                                          Load - Packet Creation Rate(sec)
Throughput Vs. Learning Rate

                           220
                                 DRQR
                           219
                                 QR
   Throughput(Kbits/sec)




                           218

                           217

                           216

                           215

                           214

                           213

                           212
                                 0.1    0.3          0.5            0.7   0.9
                                              Learning Rate (sec)
Average Packet Delay vs. Learning Rate


                              0.68
                                           DRQR
                              0.66         QR
     Avg. Packet Delay(sec)




                              0.64

                              0.62

                               0.6

                              0.58

                              0.56

                              0.54
                                     0.1    0.3           0.5           0.7   0.9
                                                  Learning Rate (sec)
    Conclusions

   Q-Routing does not always guarantee to find the shortest path.

   At high system load QR display lower average packet delay compared
    with DRQR and SPRA

   Low bandwidth utilization in Q Routing and packet overhead in DRQR –
    can be overcome by generating learning packets only when the queues
    get filled to a threshold

   Shortest path algorithms ignores the bottleneck as the network traffic
    increases.

   The need for an efficient adaptive routing algorithm.
Thank you

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:23
posted:4/20/2011
language:English
pages:22