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Job online scheduling within dynamic grid environment

机译:动态网格环境中的作业在线调度

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摘要

This paper proposes the idea of adaptive job scheduling algorithm by using hybrid Ant Colony Optimization (ACO) and Tabu algorithms. The idea behind the scheduling algorithm is evaluation of completion time of jobs in a service Grid. The algorithm comprises of two main techniques; first of all, Grid Information Service (GIS) collects information from each grid node, ACO evaluates complete time of jobs in possible grid nodes and then assigns job to appropriate grid node. ACO is used to minimize the average completion time of jobs through optimal job allocation on each node as well. While, Tabu algorithm is used to adjust performance of grid system because online jobs are submitted to grid system from time to time. This paper shows that the algorithm can find an optimal processor for each machine to allocate to a job that minimizes the tardiness time of a job when the job is scheduled in the system.
机译:本文提出了一种基于混合蚁群优化和禁忌算法的自适应作业调度算法的思想。调度算法背后的思想是评估服务网格中作业的完成时间。该算法包括两种主要技术:首先,网格信息服务(GIS)从每个网格节点收集信息,ACO评估可能的网格节点中作业的完整时间,然后将作业分配给适当的网格节点。 ACO还可通过在每个节点上进行最佳作业分配来最大程度地减少作业的平均完成时间。同时,Tabu算法用于调整网格系统的性能,因为在线作业会不时提交到网格系统。本文表明,该算法可以为每台机器找到分配给该作业的最佳处理器,从而在系统中调度该作业时将作业的拖延时间减至最少。

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