NDLI: An Efficient Task Scheduling Technique in Heterogeneous Systems Using Self-Adaptive Selection-Based Genetic Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Deepa, R. Srinivasan, T. Miriam, D.D.H.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Sri Venkateswara Coll. of Eng., Sriperumbudur (Deepa, R.)
Abstract	Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We propose a new genetics-based approach to scheduling parallel tasks on heterogeneous processors. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. The selection scheme used in our scheduling algorithm is designed to maintain the genetic diversity within the population by advantageous self adaptive steering of selection pressure. This self-adaptive mechanism referred to as progeny selection in which the fitness of an offspring is compared to the fitness of its own parents. The sufficient amount of `successful' offspring becomes the member of next generation. Comparison with traditional scheduling methods indicates that the new GA is competitive in terms of solution quality if it has sufficient resources to perform its search
Sponsorship	IEEE CSP
Starting Page	343
Ending Page	348
File Size	163470
Page Count	6
File Format	PDF
ISBN	0769525547
DOI	10.1109/PARELEC.2006.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-13
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Optimal scheduling Processor scheduling Genetic engineering Computer science Educational institutions Scheduling algorithm Parallel machines Algorithm design and analysis Heuristic algorithms task scheduling. Genetic algorithm heterogeneous systems parallel systems self-adaptive selection
Content Type	Text
Resource Type	Article

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