网格环境下“可处理的批量数据传输”任务的资源管理算法性能

I. Ahmad, S. Majumdar
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引用次数: 0

摘要

可处理的批量数据传输(PBDT)任务是资源密集型并发任务,涉及传输大量数据,这些数据必须以某种方式进行处理,才能在称为汇聚节点的远程目标节点集上使用。分布式计算环境(如网格)是执行这些任务的流行方式。针对网格计算环境下PBDT任务的执行,提出了一种高效的资源分配机制。由于这些任务需要大量的资源,因此有效的资源分配对于在执行这些任务的同时获得令人满意的性能至关重要。随着给定网格计算环境中可用资源数量的增加,资源分配算法的时间复杂度急剧上升,对资源的有效分配提出了挑战。为了应对这一挑战,本文研究了近似算法在资源分配中的应用。通过降低算法的复杂性而获得的好处与任务执行期间产生的增加的成本相权衡(由于近似值引入的资源分配不准确)。本文描述了一些近似值,并讨论了在什么情况下使用这些近似值。本研究提出的技术可以扩展到非pbdt任务和其他分布式计算环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance of resource management algorithms for "Processable Bulk Data Transfer" Tasks in Grid Environments
Processable Bulk Data Transfer (PBDT) tasks are resource intensive concurrent tasks which involve transfer of a very large amount of data that has to be processed in some way before it can be used at a remote set of destination nodes called the sink nodes. A distributed computing environment, such as the Grid is a popular way to perform these tasks. Focusing on the execution of PBDT tasks in a Grid computing environment, this paper presents an efficient resource allocation mechanism. Due to the resource thirsty nature of these tasks, an efficient resource allocation is essential to perform these tasks while achieving satisfactory performance. The time-complexity of the resource allocation algorithm rises sharply as the available number of resources in the given Grid computing environment is increased making efficient resource allocation a challenge. To meet this challenge, this paper investigates the use of approximate algorithms for the resource allocation. The benefits obtained by using the reduced complexity of the algorithm are weighed against the increased costs incurred during the task execution (due to the inaccuracies in resource allocation introduced by the approximations). This paper describes a number of approximations and discusses under which circumstances such approximations are to be used. The techniques presented in this research can be extended to non-PBDT tasks and other distributed computing environments.
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