支持协同分配和提前预约的网格工作流启发式调度

Jörg Decker, Jörg Schneider
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引用次数: 36

摘要

在网格计算环境中执行的应用程序变得越来越复杂,通常由多个相互依赖的任务组成。这种紧密或松散耦合任务的协调执行通常需要同时访问不同的网格资源。这就导致了资源共同分配的问题。必须开发高效、鲁棒的调度算法,以应对网格的大规模分布、大量竞争和要求苛刻的应用程序、固有的资源异构性以及通常有限的资源可用性。本文提出了两种启发式调度算法,这两种算法都是基于一种著名的列表调度算法,并且都支持协同分配和预先预留资源。我们的第一种算法保留了贪婪列表调度器的运行时效率,而第二种方法结合了更复杂的搜索技术,以便在性能指标方面获得更好的结果。这两种算法都在网格仿真框架内实现。进行了广泛的仿真研究,以评估和比较两种算法的性能。它显示了我们的增强列表调度启发式在异构网格环境中的一般适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heuristic Scheduling of Grid Workflows Supporting Co-Allocation and Advance Reservation
Applications to be executed in grid computing environments become more and more complex and usually consist of multiple interdependent tasks. The coordinated execution of such tightly or loosely coupled tasks often requires simultaneous access to different grid resources. This leads to the problem of resource co-allocation. Efficient and robust scheduling algorithms have to be developed that can cope with the Grid's large- scale distribution, a high number of competing and demanding applications, the inherent resource heterogeneity and the often limited view on resource availability. In this paper, we present two heuristic scheduling algorithms that are based on a well-known list scheduling algorithm and both support co- allocation and advance resource reservation. Our first algorithm preserves the run-time efficiency of greedy list schedulers while the second approach incorporates more sophisticated search techniques in order to achieve better results with respect to the performance metrics. Both algorithms have been implemented within a grid simulation framework. An extensive simulation study was conducted to evaluate and compare the performance of both algorithms. It showed the general suitability of our enhanced list scheduling heuristics within heterogeneous grid environments.
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