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Journal of Grid Computing

, 4:373 | Cite as

Entropic Grid Scheduling

  • Youcef Derbal
Article

Abstract

Computational Grids (CGs) are large scale dynamical networks of geographically distributed peer resource clusters. These clusters are independent but cooperating computing systems bound by a management framework for the provision of computing services, called Grid Services. In its basic form, the Grid scheduling problem consists in finding at least one cluster that has the capacity to handle, within the constraints of a specified quality of service, a user service request submitted to the CG. Since CGs span distinct management domains, the scheduling process has to be decentralized. Furthermore, it has to account for the ubiquitous uncertainty on the state of the CG. In this paper, we propose a scalable distributed Entropy-based scheduling approach that utilizes a Markov chain model to capture the dynamics of the service capacity state. An entropy-based quantification of the uncertainty on the service capacity information is developed and explicitly integrated within the proposed Grid scheduling approach. The performance of the proposed scheduling strategy is validated, through simulation, against a random delegation scheme and a load balancing-based scheduling strategy with respect to throughput, exploitation and convergence speed, respectively.

Key words

Computational Grid Decision Making Scheduling Entropy Markov Chain 

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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.School of Information Technology ManagementRyerson UniversityTorontoCanada

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