Utilizing Neural Networks to Reduce Packet Loss in Self-Similar Teletraffic Patterns

Transcription

1 Utilizing Neural Networks to Reduce Packet Loss in Self-Similar Teletraffic Patterns Homayoun Yousefi zadeh; EECS Dept; UC, Irvine Edmond A. Jonckheere; EE-Systems Dept; USC John A. Silvester; EE-Systems Dept; USC

2 Problem Statement Objective: Reducing Packet Loss in ATM Queuing Systems Considering Buffer Allocation Fairness Approach: Utilizing A Neural Network Prediction Scheme to Adjust Buffer Allocation Dynamically

3 Self-Similarity Structural Similarities across a Wide Range of Time Scales Properties Slowly Decaying Var s ( m) ( ) var( X ) m km β 2, 0< β < 1 Long Range Dependence Auto-Correlation Sum n 1/ f Noise Property γ λ k3λ = 0< γ < 1 f( λ), Rn ( ) = Teletraffic Patterns Exhibit Fractal Behavior w/ No Actual Burst Length Traffic Evidence Ethernet LAN: Leland ISDN: Hellstern VBR Video: Beran CCSN: Duffy

4 Self-Similar Teletraffic Modeling Statistical Approach Leland, Willinger, Taqq, Willson Chaotic Systems Approach Erramilli, Singh, Prutti Neural Network (NN) Approach Yousefi zadeh, Jonckheere xk [ ] Fully Connected Feed Forward BPA Learning Inputs: xk [ 8] thru xk [ 1] 3 Hidden Layers w/ 20 PEs Each Output: xk [ ] xk [ 8] xk [ 1]

5 Back Propagation Algorithm Start from an Initial Set of Weighting Functions Do { Propagate Forward from Input u to Output ϑ 1 2 Calculate Absolute Output Error E = ( uk ϑ k) 2 and k Calculate Backward Scaled Relative Errors of Each PE e [ s] = x [ s](1. x [ s]). { e [ s + 1]. w [ s + 1]} j j j k kj k Calculate per PE Variations of Weighting Functions wji[] s = lce. j[]{ s.{ xi[ s 1] + ke. i[ s 1]} } + M( wji[]) s ( k+ 1) thstep k thstep } While ( Error > Bound ) BPA Complexity: Time OIN ( ) Space ON ( )

6 Self-Similar Traffic Generation Artificial On-Off Source Model: Intermittency Maps x ε n+ 1 m ε 1+ xn+ cx 1 n, 0 xn d = m ε x c (1 x ), d x n + 2 n n = 001., ε = 005., m = c1= 173., c2 = x0 [0.,. 103], d = 0.7 Threshold-Based Transition of State from Active to Passive State and Vice-Versa NN Modeling of DIM or Normalized Continuous Traffic Samples

7 Experiment Setup Sources n=3 with 30, 40, 50 DIMs Burst Scale ATM (FIFO) Queuing Fixed Service Rate, Negligible Buffer Loss FTDM (Dedicated Service Rates, e.g., ATM VPs) or STDM (Shared Service Rates, e.g., ATM VCs) Scheduling

8 Buffer Mgmt Schemes CS: Best Total Loss Worst Total Fairness; S1=S2=S3=0 SPS; SB, S1, S2, S3 Fixed DNS; SB Fixed, S1, S2, S3 Dynamic CP: Worst Total Loss Best Total Fairness; SB=0

9 FTDM Results OIN ( ) Total Loss Most Passive Source Loss

10 STDM Results Total Loss Most Passive Source Loss

11 Numerical Issues Time Consuming Learning Algorithm due to Dynamics Complexity Iterative Learning Is Required as the Result of Facing Chaotic Divergence Impact of Initial Weighting Functions of NN in the Algorithm Convergence

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