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Overhead Analysis of Scientific Workflows in Grid Environments

机译：网格环境中科学工作流的开销分析

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Scientific workflows are a topic of great interest in the Grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area Grid infrastructures. Traditionally, the Grid workflow execution is approached as a pure best-effort scheduling problem that maps the activities onto the Grid processors based on appropriate optimisation or local matchmaking heuristics such that the overall execution time is minimised. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable Grid environments is prone to severe performance losses that must be understood for minimising the completion time or for efficient use of high- erformance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic Grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured Grid execution time based on a hierarchy of performance overheads for Grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of Grid computing, including speedup and efficiency. We present a distributed online tool for computing and analysing the performance overheads in realtime based on event correlation techniques and introduce several performance contracts, as quality of service parameters to be enforced during the workflow execution beyond traditional best-effort practices. We illustrate our method through post-mortem and online performance analysis of two real-world workflow applications executed in the Austrian Grid environment.
机译：科学工作流是Grid社区中非常感兴趣的主题，它在工作流模型中看到了一种用于对分布式广域Grid基础结构进行编程的有吸引力的范例。传统上，将Grid工作流执行作为纯粹的尽力而为调度问题来处理，该问题基于适当的优化或本地匹配试探法将活动映射到Grid处理器上，从而使总执行时间最小化。即使这种启发式方法通常可以提供有效的结果，但在动态且不可预测的Grid环境中执行该操作很容易造成严重的性能损失，必须将其理解为可以最大限度地减少完成时间或有效利用高性能资源。在本文中，我们提出了一种新的系统方法，以帮助科学家和中间件开发人员了解在动态Grid环境中执行科学工作流时出现的最严重的性能损失源。我们介绍了一种理想的模型，该模型可以通过工作流实现最短的执行时间，并根据网格计算性能开销的层次结构，说明与实际测量的Grid执行时间的差异。我们描述了如何系统地测量和计算从单个活动到较大工作流区域的开销，以及如何将众所周知的并行处理指标调整到Grid计算的范围，包括加速和效率。我们提供了一种基于事件相关技术实时计算和分析性能开销的分布式在线工具，并介绍了一些性能合同，作为在工作流执行过程中要超越传统的尽力而为的服务质量参数。我们通过对在奥地利网格环境中执行的两个实际工作流应用程序的事后分析和在线性能分析来说明我们的方法。

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