Communication-aware task scheduling and voltage selection for total energy minimization in a multiprocessor system using Ant Colony Optimization

Hyunjin Kim, Sungho Kang

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Energy consumption is a key parameter when highly computational tasks should be performed in a multiprocessor system. In this case, in order to reduce total energy consumption, task scheduling and low-power methodology should be combined in an efficient way. This paper proposes an algorithm for off-line communication-aware task scheduling and voltage selection using Ant Colony Optimization. The proposed algorithm minimizes total energy consumption of an application executing on a homogeneous multiprocessor system. The artificial agents explore the search space based on stochastic decision-making using global heuristic information with total energy consumption and local heuristic information with interprocessor communication volume. In search space exploration, both voltage selection and the dependencies between tasks are considered. The pheromone trails are updated by normalizing the total energy consumption. The pheromone trails represent the global heuristic information in order to utilize all entire energy consumption information from previous evaluated solutions. Experimental results show that the proposed algorithm outperforms traditional communication-aware task scheduling and task scheduling using genetic algorithms in terms of total energy consumption.

Original languageEnglish
Pages (from-to)3995-4008
Number of pages14
JournalInformation sciences
Volume181
Issue number18
DOIs
Publication statusPublished - 2011 Sep 15

Fingerprint

Ant colony optimization
Energy Minimization
Task Scheduling
Multiprocessor Systems
Energy Consumption
Energy utilization
Voltage
Scheduling
Communication
Electric potential
Pheromone
Heuristics
Search Space
Interprocessor Communication
Energy consumption
Energy
Scheduling algorithms
Scheduling Algorithm
Genetic algorithms
Decision making

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

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