Power management has become a critical issue for communication and computing infrastructures. In this paper, we develop systematic and heuristic algorithms for managing communication switches with multiple speed (throughput) modes. Switch speed is increased by activating additional network processor cores, voltage/frequency scaling of the electronics, etc. Higher speed modes burn more power. The developed power (speed) control algorithms are responsive to switch queue backlogs and aim to efficiently trade backlog vs. power costs, so as to minimize the long-run overall backlog and power cost. We first systematically model the system in a Dynamic Programming (DP) framework, where the optimal control can be computed in principle. Unfortunately, the complexity is so high that even for a 2 × 2 switch the computation time is practically infeasible. We therefore develop an Approximate Dynamic Programming (ADP) algorithm based on the Q-learning framework, which has good computation and performance properties. We also develop two low-complexity heuristic power controls, one based on a “myopic” view of the control problem, and the other on “congestion-awareness” of the standard maximum weight matching scheme. We study the performance of the Q-learning and heuristic controls against the optimal one (which we can practically compute only for the 2×2 switch case), and comment on their efficiency. We also extend the method and results to the general case of multiprocessor computing resources.

Due to increasing circuit densities and data throughput rates, power consumption has become a significant concern in the design and operation of high-performance packet switches. We extend the idea of Dynamic Power Management (DPM) to input queued switches, allowing operators to tradeoff power and delay in a useful way. We frame the problem as a dynamic program and solve a relaxation using techniques from Linear Quadratic Regulation (LQR). This optimal policy is combined with existing, nonpower-aware switch controls to generate two novel scheduling algorithms: 1) LQR Power Aware Maximum Weight Matching (LQR PA MWM) and 2) LQR Power Aware Projective Cone Scheduling (LQR PA PCS). Simulation results suggest that our algorithms result in significant power savings compared to MWM and previous power control schemes with little performance degradation.

This paper develops an optimal congestion control algorithm for wireless networks using TCP based on utility maximization techniques. The algorithm requires no modifications to TCP. The algorithm, termed CBW, adaptively responds to changes in network routing and topology. Several simulations are presented and indicate a substantial gain in throughput and a reduction in aggregate transmitter power. A simplified heuristic is also presented.

This paper develops an optimal congestion control algorithm for wireless networks using TCP Reno/RED based on utility maximization techniques. The algorithm requires no modifications to TCP. Several simulations are presented and indicate a substantial gain in performance. A simplified heuristic is also presented.