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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

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Abstract

Both parallel and distributed network environment systems play a vital role in the improvement of high performance computing. The primary concern when analyzing these systems is multiprocessor task scheduling. This paper addresses the problem of efficient multiprocessor task scheduling. A multiprocessor task scheduling problem is represented as directed acyclic task graph (DAG), for execution on multiprocessors with communication costs. In this paper we have investigated the effectiveness of a proposed paradigm based on genetic algorithms (GAs). GAs is a class of robust stochastic search algorithms for various combinatorial optimization problems. We have designed a GA based encoding mechanism that uses multi-chromosome encoding scheme. The implementation of the technique is simple. The performance of the designed algorithm has been tested on a variety of multiprocessor systems both heterogeneous as well as homogeneous.

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Correspondence to Poonam Panwar .

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Panwar, P., Lal, A.K., Singh, J. (2012). A Genetic Algorithm Based Technique for Efficient Scheduling of Tasks on Multiprocessor System. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_84

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  • DOI: https://doi.org/10.1007/978-81-322-0491-6_84

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

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