PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
A Scalable Solution to the Multi-Resource QoS Problem
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
A new framework for hierarchical cross-layer adaptation
A new framework for hierarchical cross-layer adaptation
Dynamic algorithm transforms for low-power reconfigurable adaptiveequalizers
IEEE Transactions on Signal Processing
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Battery-powered wireless systems running media applications have tight constraints on energy, CPU, and network capacity, and therefore require the careful allocation of these limited resources to maximize the system's performance while avoiding resource overruns. Usually, resource-allocation problems are solved using standard knapsack-solving techniques. However, when allocating conservable resources like energy (which unlike CPU and network remain available for later use if they are not used immediately) knapsack solutions suffer from excessive computational complexity, leading to the use of suboptimal heuristics. We show that use of Lagrangian optimization provides a fast, elegant, and, for convex problems, optimal solution to the allocation of energy across applications as they enter and leave the system, even if the exact sequence and timing of their entrances and exits is not known. This permits significant increases in achieved utility compared to heuristics in common use. As our framework requires only a stochastic description of future workloads, and not a full schedule, we also significantly expand the scope of systems that can be optimized.