OSCAR-P and aMLLibrary: Profiling and predicting the performance of FaaS-based applications in computing continua

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software Pub Date : 2024-11-20 DOI:10.1016/j.jss.2024.112282

Roberto Sala , Bruno Guindani , Enrico Galimberti , Federica Filippini , Hamta Sedghani , Danilo Ardagna , Sebastián Risco , Germán Moltó , Miguel Caballer

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引用次数: 0

Abstract

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environments. OSCAR-P obtains relevant profiling information on the execution time of the individual application components. These data are later used by aMLLibrary to train ML-based performance models. This makes it possible to predict the performance of applications on unseen configurations. We test our framework on clusters with different architectures (x86 and arm64) and workloads, considering multi-component use-case applications. This extensive experimental campaign proves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the time needed for the application profiling, data collection, and data processing. The preliminary results obtained on the ML performance models accuracy show a Mean Absolute Percentage Error lower than 30% in all the considered scenarios.

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OSCAR-P 和 aMLLibrary：剖析和预测计算连续性中基于 FaaS 的应用程序的性能

本文提出了一个用于高效应用剖析和训练机器学习（ML）性能模型的自动化框架，由两部分组成：OSCAR-P 和 aMLLibrary。OSCAR-P 是一款自动剖析工具，旨在自动测试在云和边缘环境中多个硬件和节点组合上运行的无服务器应用程序工作流。OSCAR-P 可获取各个应用组件执行时间的相关剖析信息。这些数据随后会被 aMLLibrary 用来训练基于 ML 的性能模型。这样就可以预测应用程序在未知配置上的性能。我们在具有不同架构（x86 和 arm64）和工作负载的集群上测试了我们的框架，并考虑了多组件用例应用程序。这一广泛的实验活动证明了 OSCAR-P 和 aMLLibrary 的效率，大大缩短了应用剖析、数据收集和数据处理所需的时间。关于 ML 性能模型准确性的初步结果显示，在所有考虑的场景中，平均绝对百分比误差均低于 30%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Systems and Software 工程技术-计算机：理论方法

CiteScore

8.60

自引率

5.70%

发文量

193

审稿时长

16 weeks

期刊介绍： The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: •Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution •Agile, model-driven, service-oriented, open source and global software development •Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems •Human factors and management concerns of software development •Data management and big data issues of software systems •Metrics and evaluation, data mining of software development resources •Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.