论文标题
无服务器容器 - 科学工作流程的可行方法上升
Serverless Containers -- rising viable approach to Scientific Workflows
论文作者
论文摘要
无服务器计算方法的普及越来越多，导致了在AWS Fargate，Google Cloud Run或Azure容器实例（AWS FARGATE）等容器-AS-Service（CAAS）模型中工作的新云基础架构的出现。他们引入了一种创新的方法，用于运行云容器，其中开发人员摆脱了管理基本资源的管理。在本文中，我们专注于评估弹性容器的功能及其在科学工作流范式中使用AWS Fargate和Google Cloud Run Run基础架构在科学计算中的有用性。为了对我们的方法进行实验评估，我们扩展了HyperFlow引擎以支持这些CAAS平台，并适应了四个现实世界中的科学工作流程，由数十几个组成，以将一百多个任务组织到依赖图中。我们使用这些工作流来创建成本效果基准和流执行图，测量延迟，弹性和可扩展性。实验证明，无服务器容器可以成功应用于科学工作流程。此外，结果使我们能够了解此类平台的特定优势和限制。
Increasing popularity of the serverless computing approach has led to the emergence of new cloud infrastructures working in Container-as-a-Service (CaaS) model like AWS Fargate, Google Cloud Run, or Azure Container Instances. They introduce an innovative approach to running cloud containers where developers are freed from managing underlying resources. In this paper, we focus on evaluating capabilities of elastic containers and their usefulness for scientific computing in the scientific workflow paradigm using AWS Fargate and Google Cloud Run infrastructures. For experimental evaluation of our approach, we extended HyperFlow engine to support these CaaS platform, together with adapting four real-world scientific workflows composed of several dozen to over a hundred of tasks organized into a dependency graph. We used these workflows to create cost-performance benchmarks and flow execution plots, measuring delays, elasticity, and scalability. The experiments proved that serverless containers can be successfully applied for scientific workflows. Also, the results allow us to gain insights on specific advantages and limits of such platforms.
