Robust optimization of Random Early Detection |
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Authors: | Rahul Vaidya Shalabh Bhatnagar |
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Affiliation: | (1) Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560012, India;(2) Present address: Samsung India Software Operations, Bangalore, India |
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Abstract: | Random Early Detection (RED) is the most widely used Adaptive Queue Management (AQM) mechanism in the internet. Although RED
shows better performance than its predecessor, DropTail, its performance is highly sensitive to parameter settings. Under
non-optimum parameter settings, the performance degrades and quickly approaches that of DropTail gateways. As the network
conditions change dynamically and since the optimum parameter settings depend on these, the RED parameters also need to be
optimized and updated dynamically. Since the interaction between RED and TCP is not well understood as analytical solutions
cannot be obtained, stochastic approximation based parameter optimization is proposed as an alternative. However, simulation
based approaches may yield a sub-optimal solution since for these to work, the network needs to be accurately simulated which
is, however, infeasible with today’s internet. In this paper, we present an optimization technique for optimizing RED parameters
that makes use of direct measurements in the network. We develop a robust two-timescale simultaneous perturbation stochastic
approximation algorithm with deterministic perturbation sequences for optimization of RED parameters. A proof of convergence
of this algorithm is provided. Network simulations, using direct implementation of the algorithm over RED routers, are carried
out to validate the proposed approach. The algorithm presented here is found to show better performance as compared to a recently
proposed algorithm that adaptively tunes a RED parameter. |
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Keywords: | Random early detection (RED) Adaptive queue management Transmission control protocol (TCP) Simultaneous perturbation stochastic approximation (SPSA) Robust parameter optimization The sign function |
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