Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Task scheduling and voltage selection for energy minimization
Proceedings of the 39th annual Design Automation Conference
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Hybrid Genetic Algorithms for Scheduling Partially Ordered Tasks in a Multi-Processor Environment
RTCSA '99 Proceedings of the Sixth International Conference on Real-Time Computing Systems and Applications
ASP-DAC '02 Proceedings of the 2002 Asia and South Pacific Design Automation Conference
Energy Aware Scheduling for Distributed Real-Time Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems
Proceedings of the conference on Design, automation and test in Europe
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Genetic-algorithm-based real-time task scheduling with multiple goals
Journal of Systems and Software - Special issue: Computer systems
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
The Journal of Supercomputing
A study on combinational effects of job and resource characteristics on energy consumption
Multiagent and Grid Systems
Parallel Training of An Improved Neural Network for Text Categorization
International Journal of Parallel Programming
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Many of today's embedded systems, such as wireless and portable devices rely heavily on the limited power supply. Therefore, energy efficiency becomes one of the major design concerns for embedded systems. The technique of dynamic voltage scaling (DVS) can be exploited to reduce the power consumption of modern processors by slowing down the processor speed. The problem of static DVS scheduling in distributed systems such that the energy consumption of the processors is minimize while guaranteeing the timing constraints of the tasks is an NP hard problem. Previously, we have developed a heuristic search algorithm: Genetic Algorithm (GA) for the DVS scheduling problem. This paper describes a Parallel Genetic Algorithm (PGA) that improves over Genetic Algorithm (GA) for finding better schedules with less time by parallelizing the GA algorithms to run on a cluster. A hybrid parallel algorithm is also developed to further improve the search ability of PGA by combining PGA with the technique of Simulated Annealing (SA). Experiment results show that the energy consumption of the schedules found by the PGA can be significantly reduced comparing to those found by GA.