面向网络感知网格调度的分布式交叉熵ANT算法

Hu Yi, Gong Bin
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引用次数: 1

摘要

网格调度是对资源进行优化分配,以实现资源利用率最大化的一种调度方法。针对多约束调度问题,提出一种基于交叉熵的分布式蚁群算法。这是一种鲁棒性极强的罕见事件模拟技术,可用于解决复杂的组合优化问题。针对网络感知网格调度问题的要求,对CE-ANT方法进行了改进。它展示了如何通过区分数据密集型和计算密集型作业并根据计算资源和网络负载调度这些作业来改进任务响应时间。仿真结果表明,与蚁群算法(Min-Min)相比,该算法能将总处理时间减少至少10%,并能在开销和收敛速度方面快速找到最优解。它在网格站点和任务数量方面都具有高度可扩展性,实际上,随着数量的增加,它比现有算法提供了更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A distributed cross-entropy ANT algorithm for network-aware grid scheduling
Grid scheduling is one of optimally assigning jobs to resources to achieve maximizing the utilization of resources. We propose a distributed ant colony algorithm based on cross-entropy for multi-constraints scheduling. This is an extremely robust rare event simulation technique which may be employed to solve difficult combinatorial optimization problems. We tailor the CE-ANT method for the requirements of network-aware grid scheduling problem. It shows how the task response time can be improved by distinguishing between data-intensive and compute-intensive jobs and scheduling these jobs based on both computational resources and network load. The simulation result demonstrates that the proposed approach succeeds in minimizing the total processing time by at least 10% as compared to its counterpart (Min-Min), and quickly finding the optimal solutions with respect to overhead and speed of convergence compared with ACO. It is highly scalable both in terms of grid site and the number of tasks, indeed it provides superior performance over existing algorithms as the number increase.
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