Abstract

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.

中文翻译：

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动态集群容错技术的面向数据的调度，用于云中的科学工作流

摘要

云计算是最杰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。通过云计算评估被构造为科学工作流程的大规模科学应用程序。科学工作流程是数据密集型应用程序，因为单个科学工作流程可能包含数十万个任务。任务失败，截止日期限制，预算限制和任务管理不当也会造成不便。因此，通过面向数据的调度提供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集群容错技术（EDS-DC）的增强型面向数据的调度，用于在云计算中执行科学的工作流程。我们已经提出了面向数据的调度作为一种建议的调度技术。我们还为EDS-DC配备了动态集群容错技术。为了了解EDS-DC的有效性，我们将其结果与三种众所周知的增强启发式调度策略进行了比较，这些策略称为：（a）MCT-DC，（b）Max-min-DC和（c）Min-min- DC。我们将Cyber​​Shake的科学工作流程视为案例研究，因为它包含科学工作流程的大多数特征，例如集成，分解，并行和流水线。结果表明，与MCT-DC相比，EDS-DC的制造跨度降低了10.9％，与Max-min-DC相比降低了13.7％，而6。与Min-min-DC调度策略相比为4％。同样，与MCT-DC相比，EDS-DC的成本降低了4％，与Max-min-DC相比降低了5.6％，与Min-min-DC调度策略相比降低了1.5％。与上述三种调度策略相比，EDS-DC在制造时间和成本方面的这些结果非常重要。就时间和成本约束而言，EDS-DC不会违反SLA，而MCT-DC，Max-min-DC和Min-min-DC调度技术会违反SLA的次数。