Control of Communication Networks: Modeling, Simulation, and Optimization

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This proposal describes a novel approach for designing network control algorithms that incorporate online simulation using traffic models. The approach facilitates rapid heuristic analysis of simulation data, and can be applied to a wide variety of network control problems, and can be applied to a wide variety of network decision problems. The PIs have proof-of-concept implementations of this approach for two simple problems: the multiclass weighted scheduling problem, and the problem of selecting a dropping policy for random early detection (RED). In both cases, they have empirically demonstrated substantially superior performance when comparing to previous control policies. The PIs propose the substantial exploration, analysis, and implementation of their technique. They plan to apply these ideas to network decision problems including admission/access control, flow/congestion control, various problems relating to proxy services, and selection of diagnosis and recovery actions. They also plan to extend these ideas to closed-loop systems such as TCP flows, as well as to evaluate the issues that arise in utilizing various traffic models, including fluid-flow and long-range dependence traffic models. This plan exploits the growing work on traffic modeling, providing the potential to use such models to impact network control performance. The broad applicability of the proposed approach opens the possibility for simultaneous improvement of a wide array of control algorithms across the network. The use of this control approach together with familiar model inference techniques to update the traffic model under changing network environments (including attacks) also leads naturally to adaptive control mechanisms for the same wide variety of problems.