Abstract
Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.