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首页> 外文会议>2018 International Conference on Computing, Power and Communication Technologies >Enhanced Hyper-Heuristic Scheduling Algorithm for Cloud
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Enhanced Hyper-Heuristic Scheduling Algorithm for Cloud

机译：面向云的增强型超启发式调度算法

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Task scheduling in cloud is mainly focused to find better and optimal solutions in order to minimize the total processing time of Virtual Machines. One of the objectives is to allocate availabe resources to tasks such that the execution of tasks is completed in minimal time with efficient use of resources. Task scheduling problem in cloud is known as NP-complete. One feasible solution for these type of problems is to apply hyper-heuristics. Hyper-heuristics are high level methods for solving complex problems that works on a search space of underlying heuristics. Previous studies have shown that using hyper-heuristic scheduling algorithm in cloud computing produces improved results. In this paper an intelligent selection operator and a multi point crossover operator are introduced. The intelligent selection operator uses a time weight to penalize slower heuristics, so that better heuristics are selected. The multipoint crossover operator is used to combine two solutions to get diversified and possibly improved new solutions. The proposed approach has been implemented in CloudSim and compared against the other standard algorithms. It is observed that for large number of tasks the proposed algorithm has performed 10.67% to 20.75% better than other standard algorithms.
机译：云中的任务调度主要集中在寻找更好和最佳的解决方案，以最大程度地减少虚拟机的总处理时间。目标之一是为任务分配可用的资源，以使任务的执行在最短的时间内完成并有效利用资源。云中的任务调度问题被称为NP-complete。这些类型问题的一种可行解决方案是应用超启发式方法。超启发式算法是用于解决复杂问题的高级方法，这些方法适用于基础启发式算法的搜索空间。先前的研究表明，在云计算中使用超启发式调度算法可产生更好的结果。本文介绍了智能选择算子和多点交叉算子。智能选择运算符使用时间权重来惩罚较慢的启发式方法，以便选择更好的启发式方法。多点交叉算子用于组合两个解决方案，以获得多样化甚至可能得到改进的新解决方案。所提出的方法已在CloudSim中实现，并与其他标准算法进行了比较。可以看出，对于大量任务，所提出的算法比其他标准算法的性能提高了10.67％至20.75％。

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