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A novel task scheduling algorithm based on dynamic critical path and effective duplication for pervasive computing environment

机译:一种基于动态关键路径和有效复制的普适计算环境任务调度算法

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摘要

In order to effectively utilize massive heterogeneous resources and provide transparent computing capability to upper applications, task scheduling as the key issue of pervasive computing system becomes significantly important. Previous proposed priority and duplication based task scheduling algorithms, which can be applied in pervasive computing environment, usually have following limitations: critical path cannot be calculated accurately while neglecting the effect of resource availability in scheduling; in duplication based resource allocation stage, duplications without restriction would lead to some negative effects on final schedule length (SL). For the purpose of solving these problems, a novel task scheduling algorithm based on dynamic critical path (DCP) and effective duplication, called DCPED, is presented in this paper. In DCPED, a more accurate DCP calculation method which takes resource availability into account is introduced. Meanwhile an effective task duplication strategy is proposed to eliminate ineffective duplications and make an optimized schedule result by using space compression technique and dynamic critical path length (DCPL) based evaluation technique respectively. Finally, simulation results show that DCPED can outperform previous algorithms significantly in NSL and speedup rate metrics. Especially, it is very effective for utilizing computing resources and scheduling the fine-grain and large-scale workflow applications in pervasive computing system.
机译:为了有效利用大量异构资源并向上层应用程序提供透明的计算功能,任务调度作为普及计算系统的关键问题变得非常重要。先前提出的可用于普适计算环境中的基于优先级和重复的任务调度算法通常具有以下局限性:在忽略资源可用性在调度中的影响时,关键路径无法准确计算;在基于重复的资源分配阶段,无限制的重复将导致对最终调度长度(SL)的某些负面影响。为了解决这些问题,本文提出了一种基于动态关键路径(DCP)和有效复制的任务调度算法DCPED。在DCPED中,引入了一种更精确的DCP计算​​方法,该方法考虑了资源可用性。同时,提出了一种有效的任务复制策略,分别采用基于空间压缩技术和基于动态关键路径长度(DCPL)的评估技术,消除了无效的重复并获得了优化的调度结果。最后,仿真结果表明,DCPED在NSL和加速率指标上可以明显优于以前的算法。尤其对于在普适计算系统中利用计算资源并调度细粒度和大规模工作流应用程序非常有效。

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