Abstract
We propose a unifying framework based on configuration linear programs and ran-domized rounding, for different energy optimization problems in the dynamic speed-scaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speed scalable processors in a fully heterogeneous setting. For both the preemptive-non-migratory and the preemptive-migratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptive-non-migratory variant, we are able to improve the best known approximation ratio for the single processor non-preemptive problem. Furthermore, we show that our approach allows to obtain a constant-factor approximation algorithm for the power-aware preemptive job shop scheduling problem. Finally, we consider the min-power routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem.
Highlights
We focus on energy minimization problems in heterogeneous computing and networking environments in the dynamic speed-scaling setting
We show that rounding configuration linear programs helps in handling the heterogeneity of both the jobs and the processors
In this paper we propose a unifying framework for minimizing energy in different heterogeneous computing and networking environments
Summary
We focus on energy minimization problems in heterogeneous computing and networking environments in the dynamic speed-scaling setting. For improving the performance of modern computing systems, designers use parallelism, i.e., multiple cores running at lower frequencies but offering better performances than a single core. These systems can be either homogeneous where an identical core is used many times, or heterogeneous combining general-purpose and special-purpose cores. We first consider two variants of the heterogeneous multiprocessor preemptive problem In both cases, the execution of a job may be interrupted and resumed later. We believe that our general techniques will find further applications in energy optimization
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