A probabilistic constrained nonlinear optimization framework to optimize RED parameters

Performance Evaluation

Volumn 66, Issue 2, 2009, Pages 81-104

  Patro, Rajesh Kumar ^a   Bhatnagar, Shalabh ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Adaptive four timescale stochastic approximation; Barrier method; Modified SPSA for second order parameter updates; Monte Carlo simulations; Nonlinear optimization; Penalty method; Probabilistic constrained optimization; RED; SPSA

Indexed keywords

ADAPTIVE ALGORITHMS; APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; COMPUTER PROGRAMMING LANGUAGES; CONGESTION CONTROL (COMMUNICATION); CONSTRAINT THEORY; ELECTRIC NETWORK TOPOLOGY; MONTE CARLO METHODS; NETWORK MANAGEMENT; OPTICAL RESOLVING POWER; OPTIMIZATION; POLYNOMIAL APPROXIMATION; PROBABILITY; QUEUEING NETWORKS; QUEUEING THEORY; STOCHASTIC CONTROL SYSTEMS;

ADAPTIVE FOUR-TIMESCALE STOCHASTIC APPROXIMATION; BARRIER METHOD; MODIFIED SPSA FOR SECOND-ORDER PARAMETER UPDATES; MONTE CARLO SIMULATIONS; NONLINEAR OPTIMIZATION; PENALTY METHOD; PROBABILISTIC CONSTRAINED OPTIMIZATION; RED; SPSA;

CONSTRAINED OPTIMIZATION;

EID: 58149350016     PISSN: 01665316     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.peva.2008.09.003     Document Type: Article

References (33)

1. T. Alemu, Évaluation des Performances des Mécanismes de Qualité de Service dans l'Internet, Thèse de doctorat, Université de Montpellier II, 2004
2. Athuraliya S., Li V.H., Low S.H., and Yin Q. REM: Active queue management. IEEE Network 15 3 (2001) 48-53
3. Bhatnagar S. Adaptive Newton-based multivariate smoothed functional algorithms for simulation optimization. ACM Transactions on Modeling and Computer Simulation 18 1 (2007) 2:1-2:35
4. Bhatnagar S. Adaptive multivariate three-timescale stochastic approximation algorithms for simulation based optimization. ACM Transactions on Modeling and Computer Simulation 15 1 (2005) 74-107
5. Bhatnagar S., Fu M.C., Marcus S.I., and Wang I.J. Two-timescale simultaneous perturbation stochastic approximation using deterministic perturbation sequences. ACM Transactions on Modeling and Computer Simulation 13 4 (2003) 180-209
6. M. Christiansen, K. Jeffay, D. Ott, F.D. Smith, Tuning RED for web traffic, in: Proceedings of INFOCOM'99
7. Dentcheva D., Lai B., and Ruszczynski A. Efficient point methods for probabilistic optimization problems. Mathematical Models of Operations Research 60 (2004) 331-346
8. W. Feng, D. Kandlur, D. Saha, K.G. Shin, Techniques for eliminating packet loss in congested TCP/IP network, Technical Report, U. Michigan, CSE-TR-349-97, November 1997
9. Fletcher R. Practical Methods of Optimization. second ed. (2000), John Wiley & Sons
10. Floyd S., and Jacobson V. Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking 1 4 (1993) 397-413
11. S. Floyd, R. Gummadi, S. Shenker, Adaptive RED: An algorithm for increasing the robustness of RED, Technical Report, The ICSI Center for Internet Research Berkeley, CA, 2001
12. M.S. Gockenbach, Analysis of the logarithmic barrier method, Preprint. URL: http://www.math.mtu.edu/
13. Hollot C., Misra V., Towsley D., and Gong W. Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Transactions on Automatic Control 47 6 (2001) 1726-1734
14. Hollot C., Misra V., Towsley D., and Gong W. On designing improved controllers for AQM routers supporting TCP flows. INFOCOM 3 (2001) 1726-1734
15. Kiefer E., and Wolfowitz J. Stochastic estimation of the maximum of a regression function. Annals of Mathematical Statistics 23 (1952) 462-466
16. Kunniyur S., and Srikant R. An Adaptive Virtual Queue (AVQ) Algorithm for Active Queue Management. IEEE Transactions on Networking 12 2 (2004) 123-134
17. S Kunniyur, R. Srikant, Analysis and design of an Adaptive Virtual Queue (AVQ) Algorithm for Active Queue Management, in: Proceedings of SIGCOMM'01, August 2001, pp. 123-134
18. Kushner H.J., and Yin G.G. Stochastic Approximation Algorithms and Applications (1997), Springer Verlag, New York
19. Luenberger D.G. Linear and Nonlinear Programming. second ed. (1984), Addison-Wesley Publishing Company, Inc.
20. ns2 Simulator, 2002. Network simulator. http://www-mash.cs.berkley.edu/ns, UCB/LBL/VINT
21. R.K. Patro, S. Bhatnagar, Four-timescale stochastic approximation for constrained RED optimization based on barrier and penalty methods, Technical Report, Dept. of Computer Science and Automation, Indian Institute of Science, 2008. URL: http://drona.csa.iisc.ernet.in/~shalabh/pubs/techRep.pdf
22. Pemantle R. Nonconvergence to unstable points in urn models and stochastic approximation. The Annals of Probability 18 (1990) 698-712
23. Rao S.S. Engineering Optimization (1996), John Wiley & Sons, Inc.
24. Robbins H., and Monro S. A stochastic approximation method. Annals of Mathematical Statistics 22 (1951) 400-407
25. Shapiro A., Kim J., and Tito H. Conditioning of convex piecewise linear stochastic programs. Mathematical Programming, Ser. A 94 (2002) 1-19
26. Spall J.C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control 37 (1992) 332-341
27. Spall J.C. Adaptive stochastic approximation by the simultaneous perturbation method. IEEE Transactions on Automatic Control 45 (2000) 1839-1853
28. Taha H.A. Operations Research: An Introduction. seventh ed. (2003), Pearson Education
29. Tuan A.T., and Molnar S. A comprehensive performance analysis of random early detection mechanism. Telecommunication Systems 25 1-2 (2004) 9-31
30. A. Uri, Penalty, barrier and augmented Lagrangian methods, Preprint. URL: www.cs.ubc.ca/spider/ascher/542/chap10.pdf
31. Vaidya R., and Bhatnagar S. Robust Optimization of Random Early Detection. Telecommunication Systems 33 4 (2006) 291-316
32. I. Wang, J.C. Spall, Stochastic optimization with inequality constraints using simultaneous perturbations and penalty functions, in: Proceedings of the 42nd IEEE Conference on Decision and Control, vol. 16, 2003, pp. 397-409
33. Zhu X., and Spall J.C. A modified second-order SPSA optimization algorithm for finite samples. International Journal of Adaptive Control and Signal Processing 16 (2002) 397-409