性能需求的模型驱动设计

A. García-Domínguez, I. Medina-Bulo, M. Marcos-Bárcena
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引用次数: 0

摘要

如果每个任务的需求都定义得很好,那么获得工作流的预期性能就会简单得多。然而,大多数时候,并不是所有的任务都有定义良好的需求,这些需求必须手工导出。对于复杂的工作流,这可能是一个容易出错且耗时的过程。在这项工作中,我们提出了一种算法,该算法可以利用可用的任务和工作流期望来导出工作流中每个任务的时间限制。该算法为每个任务分配所需的最小时间,并根据用户设置的权值分配空闲时间,同时检查任务和工作流期望是否一致。该算法通过在每条边上逐步构建结果,避免了对工作流中的每条路径进行评估。我们已经在模型处理语言中实现了该算法,并对其进行了测试,该算法对所有路径进行了评估。我们的增量算法比穷举算法在更短的时间内报告相同的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-Driven Design of Performance Requirements
Obtaining the expected performance of a workflow is much simpler if the requirements for each of its tasks are well defined. However, most of the time, not all tasks have well-defined requirements, and these must be derived by hand. This can be an error-prone and time consuming process for complex workflows. In this work, we present an algorithm which can derive a time limit for each task in a workflow, using the available task and workflow expectations. The algorithm assigns the minimum time required by each task and distributes the slack according to the weights set by the user, while checking that the task and workflow expectations are consistent with each other. The algorithm avoids having to evaluate every path in the workflow by building its results incrementally over each edge. We have implemented the algorithm in a model handling language and tested it against a naive exhaustive algorithm which evaluates all paths. Our incremental algorithm reports equivalent results in much less time than the exhaustive algorithm.
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