Paper Info
Title
On multi-processor speed scaling with migration: extended abstract
Abstract
We investigate a very basic problem in dynamic speed scaling where a sequence of jobs, each specified by an arrival time, a deadline and a processing volume, has to be processed so as to minimize energy consumption. Previous work has focused mostly on the setting where a single variable-speed processor is available. In this paper we study multi-processor environments with m parallel variable-speed processors assuming that job migration is allowed, i.e. whenever a job is preempted it may be moved to a different processor. We first study the offline problem and show that optimal schedules can be computed efficiently in polynomial time. In contrast to a previously known strategy, our algorithm does not resort to linear programming. We develop a fully combinatorial algorithm that relies on repeated maximum flow computations. The approach might be useful to solve other problems in dynamic speed scaling. For the online problem, we extend two algorithms Optimal Available and Average Rate proposed by Yao et al. [16] for the single processor setting. We prove that Optimal Available is αα-competitive, as in the single processor case. Here α1 is the exponent of the power consumption function. While it is straightforward to extend Optimal Available to parallel processing environments, the competitive analysis becomes considerably more involved. For Average Rate we show a competitiveness of (3\α)α/2 + 2α.
Year
DOI
Venue
2011
10.1145/1989493.1989539
SPAA
Keywords
Field
DocType
algorithms optimal available,average rate,m parallel variable-speed processor,multi-processor speed scaling,dynamic speed scaling,basic problem,single processor setting,optimal available,different processor,single processor case,single variable-speed processor,polynomial time,energy efficiency,linear program,online algorithm,maximum flow,parallel processing,energy efficient,competitive analysis
Online algorithm,Computer science,Parallel computing,Schedule,Linear programming,Maximum flow problem,Time complexity,Energy consumption,Computation,Distributed computing,Competitive analysis
Conference
Citations 
PageRank 
References 
26
1.03
15
Authors
3
Name
Order
Citations
PageRank
Susanne Albers11538107.42
Antonios Antoniadis212713.81
Gero Greiner3824.48