Global Optimization Based on Hybrid Clonal Selection Genetic Algorithm for Task Scheduling

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. Resource management and task scheduling is a crucial problem in Grid environments. The aim of task scheduling is to take full advantage of grid resource and execute user's task request as early and quickly as possible. We present an optimization model for the task scheduling problem and develop a hybrid clonal selection genetic algorithm (HCSGA) to effectively solve it. HCSGA first generates a new group of individuals through clone, and than crossover/selection independently all the generated individuals respectively. From the analysis and experiment result, it is concluded that HCSGA has the characteristics of rapid convergence, good global search capacity, and is superior to other algorithm simultaneously.

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