Energy Efficient Scheduling and Routing via Randomized Rounding

TL;DR

This paper introduces a unified framework using configuration linear programs and randomized rounding to optimize energy consumption across various scheduling and routing problems in heterogeneous computing and networking environments.

Contribution

It presents a novel framework that achieves near-optimal solutions for multiple energy minimization problems, improving approximation ratios in heterogeneous and network settings.

Findings

Achieves near-optimal solutions for scheduling on heterogeneous processors.

Improves approximation ratios for non-preemptive single processor scheduling.

Provides a constant-factor approximation for power-aware job shop scheduling.

Abstract

We propose a unifying framework based on configuration linear programs and randomized 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.