WfBench: Automated Generation of Scientific Workflow Benchmarks
WfBench: Automated Generation of Scientific Workflow Benchmarks
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DOI：
10.1109/pmbs56514.2022.00014
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
T. Coleman;H. Casanova;K. Maheshwari;L. Pottier;Sean R. Wilkinson;J. Wozniak;F. Suter;M. Shankar;Rafael Ferreira da Silva
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作者：
T. Coleman;H. Casanova;K. Maheshwari;L. Pottier;Sean R. Wilkinson;J. Wozniak;F. Suter;M. Shankar;Rafael Ferreira da Silva
具有高计算需求的科学工作流的流行要求它们在各种分布式计算平台上执行，包括大规模领导级高性能计算（HPC）集群。为了处理工作流执行的部署、监视和优化，在过去十年中开发了许多工作流系统。有必要使用工作流基准来评估工作流系统在当前和未来的软件堆栈和硬件平台上的性能。我们提供了一个实际工作流基准规范的生成器，可以将其转换为基准代码，以便在当前工作流系统中执行。我们的方法生成具有任意性能特征（CPU、内存和I/O使用情况）的工作流任务，以及基于生产工作流中看到的实际任务依赖结构。我们提供的实验结果表明，我们的方法生成了代表生产工作流的基准，并进行了一个案例研究，以演示我们生成的基准的使用和有用性，以评估不同配置场景下工作流系统的性能。
The prevalence of scientific workflows with high computational demands calls for their execution on various distributed computing platforms, including large-scale leadership-class high-performance computing (HPC) clusters. To handle the deployment, monitoring, and optimization of workflow executions, many workflow systems have been developed over the past decade. There is a need for workflow benchmarks that can be used to evaluate the performance of workflow systems on current and future software stacks and hardware platforms.We present a generator of realistic workflow benchmark specifications that can be translated into benchmark code to be executed with current workflow systems. Our approach generates workflow tasks with arbitrary performance characteristics (CPU, memory, and I/O usage) and with realistic task dependency structures based on those seen in production workflows. We present experimental results that show that our approach generates benchmarks that are representative of production workflows, and conduct a case study to demonstrate the use and usefulness of our generated benchmarks to evaluate the performance of workflow systems under different configuration scenarios.