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- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2011 by ACM Inc.
- ISSN
- 1556-4665
- DOI
- 10.1145/2019583.2019585
- Publisher site
- See Article on Publisher Site
Abstract
ScatterD: Spatial Deployment Optimization with Hybrid Heuristic/Evolutionary Algorithms JULES WHITE, Virginia Tech BRIAN DOUGHERTY, CHRIS THOMPSON, and DOUGLAS C. SCHMIDT, Vanderbilt University Distributed real-time and embedded (DRE) systems can be composed of hundreds of software components running across tens or hundreds of networked processors that are physically separated from one another. A key concern in DRE systems is determining the spatial deployment topology, which is how the software components map to the underlying hardware components. Optimizations, such as placing software components with high-frequency communications on processors that are closer together, can yield a number of important bene ts, such as reduced power consumption due to decreased wireless transmission power required to communicate between the processing nodes. Determining a spatial deployment plan across a series of processors that will minimize power consumption is hard since the spatial deployment plan must respect a combination of real-time scheduling, fault-tolerance, resource, and other complex constraints. This article presents a hybrid heuristic/evolutionary algorithm, called ScatterD, for automatically generating spatial deployment plans that minimize power consumption. This work provides the following contributions to the study of spatial deployment optimization for power consumption minimization: (1) it combines heuristic bin-packing with an evolutionary algorithm to produce
Journal
ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery
Published: Sep 1, 2011