# Traffic Engineering (TE) by dib16550

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Traffic Engineering (TE)
Dynamically reconfiguring the network topologies or routes so as
to effectively accommodate current traffic
Estimating current traffic matrices                                         Example：Optical layer Traffic Engineering (TE)
Environment： IP-over-WDM networks
accurately by using long-term                                                                      p
Construct optical p           physical network
paths over p y
variations information                                                                 A set of optical paths forms a virtual network topology for IP
network
Method：
Reconfigure the optical paths according to the current traffic
Yuichi Ohsita
Osaka Univ.

Input of Traffic Engineering Methods
Traffic Matrix Estimation
Current traffic demands between all nodes （traffic matrices）                               Traditional estimation methods：
Estimate from the equations
Construct fully meshed label switched paths (LSPs)
X = AT
Count packets of each flow                                                               A： Routing matrix
Require non-negligible amount of CPU resources at the edge nodes                    T: Traffic matrix                                Link load is sum of the
Estimated traffic matrices include                  amounts of traffic traversing
Estimate traffic matrices from link loads                                                     The number of equations is much smaller than the number
Link loads are monitored more easily even in large network                               of entries in traffic matrix

The estimation errors degrade the performance of
traffic engineering

Using the Additional Information                                                           Necessity of Consideration of Traffic Variations
Method to increase the accuracy of estimation by using
additional measurements                                                                   If it takes a long time to obtain sufficient number
The additional information is obtained by using the                                   of measurements…
measurements both before and after traffic engineering [12]
Current traffic may differ from the initially monitored
traffic
TM Estimation             Estimated TM            Traffic Engineering
We need to consider the traffic variations
Volume

⎡ X (n − M + 1) ⎤ ⎡ A(n − M + 1)⎤
⎢                                 ⎥T X (n) ： Link to the equations
M      ⎥=⎢        M
・・・・・・・・・・・・
⎢               ⎥ ⎢               ⎥ A (n ) ： Routing matrix at time n
⎢
⎣      X (n)    ⎥ ⎢ A(n) ⎥
⎦ ⎣               ⎦                                                                                                                                        Time
Monitor
Estimated Traffic Matrices,” in Proc. INFOCOM 2007, May 2007                        5                                      Broadnets 2008                                6

1
Goal of this work                                                                           Overview of proposed method
(1) Estimate long-term variations
Estimate traffic matrices accurately by collaborating                                                                                   ・Model the variations as periodical functions
・Estimate the parameters in the model
with traffic engineering                                                                                                                   by using the both previously and currently monitored

betw
Traff Volume
Obtain the additional information by using the route

ween OD pair
fic
h             d by traffic    i
changes caused b t ffi engineering  i
Use the additional information considering the traffic
variations

Time
(2) Adjust the estimated                                         (３)If the estimated long-term variation
variations so as to fit the                                      does not match the current variations
・Re-estimate the long-term variations

Estimation of Long-term Variations                                                          Adjustment of the Estimated Long-term Variations

We model the long-term variations as periodical                                                  Obtain the current traffic matrices which fits the
By using the Fourier series expansion, the periodic                                        long-term variations
functions are represented as

g       g
Range fitting the
Traffic between nodes i and j at time n                  Cycle
We estimate by setting       so as to fit all the link                                                                                                                        Estimated
matrix

where
Estimated long-term
variations
[14] A. Soule, A. Nucci, R. Cruz, E. Leonardi, and N. Taft, “Estimating dynamic
traffic matrices by using viable routing changes,” IEEE/ACM Transactions on
Networking, vol. 13, pp. 485–498, June 2007.

Handling the Change of Variations                                                           Detection of the Change of Variations
When traffic variations change                                                                Detect the change when | tiestj (n) − tˆi , j (n) | is
,

Traffic changes                                        significantly larger than before.
significantly
The traffic matrices after adjustment fit the current
traffic
If the estimated long-term variations is far from the current
traffic | tiest (n) − ti , j (n) | is large
traffic,     ,j
ˆ
Monitored link loads in this period are far from the current traffic
variations                                                                                         The detection is done by Smirnov-Grubbs tests
If we use the link loads monitored before the change, we cannot
long-
estimate long-term variations so as to fit the current traffic                                                                            Detect the
| tiest (n) − ti , j (n) |
,j
ˆ
change
Steps to handle the change of variation
1.     Detect the change
2.     Delete information before the change
3.     Re-estimate the long-term variations

2
Deletion of Information before the Change                                       Evaluation
Delete only information corresponding to
the changing traffic                                                            Environment
From routing matrices                                                          Topology
EON backbone topology
A(i)    Set elements corresponding
A' (i )
to the detected traffic to 0                                    Traffic demand：
Generate by adding variations to sin functions
Remove the estimated amount of the detected                                 generated.
Estimated amount of           Optical layer TE
the detected traffic               Add optical layer paths so as to make the maximum link
utilization less than 0.7
Re-estimate long-term variations by using                                            We perform the TE method once an hour
A' (i ) and X ' (i )

Accuracy of the estimation                                                      Estimated Amount of Traffic Changing Significantly
Methods
Tomogravity method
Method using only the                                                       Method
Additional Equation method                                                       Our method (without re-estimation)
monitored at previous times but                                             Results
t     id i the traffic    i ti
not considering th t ffi variations                                           By re-estimating the long-term variations, we can estimate the
Our method (without re-estimation)                                               traffic amount even when traffic changes significantly
Our method (with re-estimation)
Metrics
∑(estimatedvalue− actualvalue)
1                                                2
(RMSE) ＝                                                                          With
numberof elements                                                                                            Without
Results                                                                             re-estimation                             re-estimation
Our method can estimate traffic matrices the most accurately
Our method can use many additional information considering the
traffic variations                                                 15                                                                          16

Maximum link utilization achieved by TE
Conclusion
using estimated traffic matrices
Methods
Tomogravity method
We propose an estimation method which uses
Method using only the
monitored at previous times but                                                                     long term
Adjust the estimated long-term variations so as to fit
Our method (without re-estimation)                                               Re-estimate the long-term variations, when the traffic
Our method (with re-estimation)                                                  variations changes significantly
Metrics                                                                          Evaluation results show that our method can
Maximum link utilization after TE performed by using the                       estimate traffic matrices accurately.
target value=0.7)
estimated traffic matrices (target value=0.7
Result
Our method (with re-estimation) can decrease the maximum link