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Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment

INFORMATION SCIENCES 2012年 214卷 1-19页

作者： Kolodziej, Joanna Khan, Samee Ullah Cracow Univ Technol Inst Comp Sci PL-31115 Krakow Poland N Dakota State Univ NDSU CIIT Green Comp & Commun Lab Fargo ND 58108 USA

Task scheduling and resource allocation are the key rationale behind the computational grid. Distributed resource clusters usually work in different autonomous domains with their own access and security policies that have a great impact on the successful task execution across the domain boundaries. Heuristics and metaheuristics are the effective technologies for scheduling in grids due to their ability to deliver high quality solutions in reasonable time. In this paper, we develop a Hierarchic Genetic Scheduler (HGS-Sched) for improving the effectiveness of the single-population genetic-based schedulers in the dynamic grid environment. The HGS-Sched enables a concurrent exploration of the solution space by many small dependent populations. We consider a bi-objective independent batch job scheduling problem with makespan and flowtime minimized in hierarchical mode (makespan is a dominant criterion). The empirical results show the high effectiveness of the proposed method in comparison with the mono-population and hybrid genetic-based schedulers. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Scheduling Computational grid Genetic algorithm Hierarchic Genetic Strategy expected time to compute matrix model

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Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

引用

FUTURE GENERATION computeR SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2011年第8期27卷 1035-1046页

作者： Kolodziej, Joanna Xhafa, Fatos Univ Bielsko Biala Dept Math & Comp Sci PL-43309 Bielsko Biala Poland Tech Univ Catalonia Dept Languages & Inforrnat Syst Barcelona 08034 Spain

Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAS) which differs from the other genetic methods by its capability of concurrent search of the solution space. In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Scheduling Computational grid Genetic algorithm Hierarchic genetic strategy expected time to compute matrix model Grid simulation

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