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Data-Oriented Scheduling with Dynamic-Clustering Fault-Tolerant Technique for Scientific Workflows in Clouds

机译:带有动态聚类容错技术的数据导向调度,用于云中的科学工作流程

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Cloud computing is one of the most prominent parallel and distributed computing paradigm. It is used for providing solution to a huge number of scientific and business applications. Large scale scientific applications which are structured as scientific workflows are evaluated through cloud computing. Scientific workflows are data-intensive applications, as a single scientific workflow may consist of hundred thousands of tasks. Task failures, deadline constraints, budget constraints and improper management of tasks can also instigate inconvenience. Therefore, provision of fault-tolerant techniques with data-oriented scheduling is an important approach for execution of scientific workflows in Cloud computing. Accordingly, we have presented enhanced data-oriented scheduling with Dynamic-clustering fault-tolerant technique (EDS-DC) for execution of scientific workflows in cloud computing. We have presented data-oriented scheduling as a proposed scheduling technique. We have also equipped EDS-DC with Dynamic-clustering fault-tolerant technique. To know the effectiveness of EDS-DC, we compared its results with three well-known enhanced heuristic scheduling policies referred to as: (a) MCT-DC, (b) Max-min-DC, and (c) Min-min-DC. We considered scientific workflow of CyberShake as a case study, because it contains most of the characteristics of scientific workflows such as integration, disintegration, parallelism, and pipelining. The results show that EDS-DC reduced make-span of 10.9% as compared to MCT-DC, 13.7% as compared to Max-min-DC, and 6.4% as compared to Min-min-DC scheduling policies. Similarly, EDS-DC reduced the cost of 4% as compared to MCT-DC, 5.6% as compared to Max-min-DC, and 1.5% as compared to Min-min-DC scheduling policies. These results in respect of make-span and cost are highly significant for EDS-DC as compared with above referred three scheduling policies. The SLA is not violated for EDS-DC in respect of time and cost constraints, while it is violated number of times for MCT-DC, Max-min-DC, and Min-min-DC scheduling techniques.
机译:云计算是最突出的并行和分布式计算范例之一。它用于为大量科学和商业应用提供解决方案。通过云计算评估为科学工作流的大规模科学应用。科学工作流是数据密集型应用,因为单一的科学工作流程可能由数百个任务组成。任务故障,截止日期约束,预算限制和不正确的任务管理也可以煽动不便。因此,提供具有数据导向调度的容错技术是在云计算中执行科学工作流的重要方法。因此,我们介绍了具有动态聚类容错技术(EDS-DC)的增强的数据定向调度,用于在云计算中执行科学工作流程。我们已经将面向数据的调度作为建议的调度技术呈现。我们还配备了具有动态聚类容错技术的EDS-DC。要知道EDS-DC的有效性,我们将其结果与三个众所周知的增强的启发式调度策略进行了比较:(a)MCT-DC,(B)MAX-MIN-DC,(C)min-min- DC。我们将科学工作流程视为一个案例研究,因为它包含了科学工作流程的大部分特征,如集成,崩解,并行和流水线。结果表明,与MCT-DC相比,EDS-DC减少了10.9%的产量,而与MAX-MIN-DC相比,与MIN-MIN-DC相比,与Min-Min-DC调度策略相比的6.4%。类似地,与MCT-DC相比,EDS-DC与MCT-DC相比,与MAX-MIN-DC相比的5.6%,与Min-Min-DC调度策略相比,1.5%。与上面提到的三个调度政策相比,这些导致制造跨度和成本的结果非常重要。在时间和成本约束的情况下,SLA不会违反EDS-DC,而MCT-DC,MAX-MIN-DC和MIN-MIN-DC调度技术则违反次数。

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  • 来源
    《Programming and Computer Software》 |2019年第8期|506-516|共11页
  • 作者单位

    Hazara Univ Dept Informat Technol Front Multipurpose Hall Mansehra 21120 Khyber Pakhtunk Pakistan;

    Hazara Univ Dept Informat Technol Front Multipurpose Hall Mansehra 21120 Khyber Pakhtunk Pakistan;

    Hazara Univ Dept Informat Technol Front Multipurpose Hall Mansehra 21120 Khyber Pakhtunk Pakistan;

    Hazara Univ Dept Informat Technol Front Multipurpose Hall Mansehra 21120 Khyber Pakhtunk Pakistan;

    Hazara Univ Dept Informat Technol Front Multipurpose Hall Mansehra 21120 Khyber Pakhtunk Pakistan;

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