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Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing

机译:云计算中静态元任务调度的增强负载均衡最小-最小算法

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For today's most demanding service of cloud computing, there are many tasks required to be executed by the available resources to achieve best performance, reduce response time and utilize resources. There is a need of designing a new task scheduling algorithm that outperform appropriate allocation map of task. to achieve these challenges. As Load Balanced Min-Min Algorithm selects the task with minimum completion time and assigns it to appropriate resource, it sometimes does not produce better makespan and does not utilize resources effectively. This paper represents study of variety of task scheduling algorithms and modification of Load balanced Min-Min (ELBMM) algorithm for Static Meta-Task Scheduling. The modified algorithm is built based on comprehensive study of the impact of Load balanced Min-Min algorithm for Static Meta-Task Scheduling in grid computing. Enhanced Load balanced Min-Min algorithm (ELBMM) is based on Min-Min strategy and tasks rescheduling to use the unutilized resources effectively. It selects the task with maximum completion time and assigns it to appropriate resource to produce better makespan and utilize resource effectively.
机译:对于当今最苛刻的云计算服务,可用资源需要执行许多任务才能获得最佳性能,减少响应时间并利用资源。需要设计一种新的任务调度算法,该算法优于适当的任务分配图。应对这些挑战。由于负载平衡的最小-最小算法会选择完成时间最短的任务并将其分配给适当的资源,因此有时它不会产生更好的有效期,也无法有效利用资源。本文介绍了各种任务调度算法的研究以及对静态元任务调度的负载均衡最小-最小(ELBMM)算法的修改。在全面研究负载均衡最小-最小算法对网格计算中静态元任务调度的影响的基础上,构建了改进的算法。增强的负载均衡的Min-Min算法(ELBMM)基于Min-Min策略和重新计划任务以有效利用未利用的资源。它选择具有最大完成时间的任务,并将其分配给适当的资源以产生更好的有效期并有效地利用资源。

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