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.

翻译：本文提出了一种用于高效应用性能分析与机器学习性能模型训练的自动化框架，该框架由两部分组成：OSCAR-P与aMLLibrary。OSCAR-P是一种自动化性能分析工具，旨在自动测试运行于云边环境中多种硬件与节点组合上的无服务器应用工作流。OSCAR-P能够获取各应用组件执行时间的相关性能分析信息。这些数据随后由aMLLibrary用于训练基于机器学习的性能模型，从而实现对未见过配置的应用性能进行预测。我们在具有不同架构（x86与arm64）和负载的集群上测试了该框架，并考虑了多组件的用例应用。这项广泛的实验验证了OSCAR-P与aMLLibrary的高效性，显著减少了应用性能分析、数据收集与数据处理所需的时间。在机器学习性能模型准确性方面获得的初步结果表明，在所有考虑的场景中，平均绝对百分比误差均低于30%。