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Static Batch Mode Heuristic Algorithm for Mapping Independent Tasks in Computational Grid | Science Publications

机译:计算网格中独立任务映射的静态批处理启发式算法科学出版物

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> Grid computing plays an important role in solving large-scale computational problems in a high performance computing environment. Scheduling of tasks to efficient and best suitable resource is one of the most challenging phase in grid computing systems. Grid environment reveals several challenges in efficient scheduling of complex applications because of its heterogeneity, dynamic behavior and shared resources. Scheduling of independent tasks in grid computing is dealt by a number of heuristic algorithms. This study proposes a new heuristic algorithm for mapping independent tasks in a grid environment to be assigned optimally among the available machines in a grid computing system. Due to the multi-objective nature of the grid scheduling problem, several performance measures and optimization criteria can be assumed to determine the quality of a given schedule. The metrics used here include makespan and resource utilization. This algorithm provides effective resource utilization by reducing machine idle time and minimizes makespan. This algorithm also balances load among the grid resources and produce high resource utilization with low computational complexity. The proposed algorithm is compared with other popular heuristics for performance measures.
机译: >网格计算在解决高性能计算环境中的大规模计算问题中起着重要作用。将任务调度到高效,最合适的资源是网格计算系统中最具挑战性的阶段之一。网格环境由于其异构性,动态行为和共享资源,在有效调度复杂应用程序方面面临着一些挑战。网格计算中独立任务的调度由许多启发式算法处理。这项研究提出了一种新的启发式算法,用于映射网格环境中的独立任务,以便在网格计算系统中的可用机器之间进行最佳分配。由于网格调度问题的多目标性质,可以采用几种性能度量和优化标准来确定给定调度的质量。此处使用的指标包括制造期和资源利用率。该算法通过减少机器的空闲时间并最大程度地缩短了制造时间,从而有效地利用了资源。该算法还可以平衡网格资源之间的负载,并以较低的计算复杂度产生较高的资源利用率。将该算法与其他流行的启发式算法进行性能比较。

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