A Multi-objective Framework for Effective Performance Fault Injection in Distributed Systems

L. Traini
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Abstract

Modern distributed systems should be built to anticipate performance degradation. Often requests in these systems involve ten to thousands Remote Procedure Calls, each of which can be a source of performance degradation. The PhD programme presented here intends to address this issue by providing automated instruments to effectively drive performance fault injection in distributed systems. The envisioned approach exploits multi-objective search-based techniques to automatically find small combinations of tiny performance degradations induced by specific RPCs, which have significant impacts on the user-perceived performance. Automating the search of these events will improve the ability to inject performance issues in production in order to force developers to anticipate and mitigate them.
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分布式系统中有效性能故障注入的多目标框架
现代分布式系统的构建应该考虑到性能的下降。这些系统中的请求通常涉及10到数千个远程过程调用,每个调用都可能导致性能下降。这里介绍的博士课程打算通过提供自动化仪器来有效地驱动分布式系统中的性能故障注入来解决这个问题。设想的方法利用基于多目标搜索的技术,自动发现由特定rpc引起的微小性能下降的小组合,这些性能下降对用户感知的性能有重大影响。自动化搜索这些事件将提高在生产环境中引入性能问题的能力,从而迫使开发人员预测并缓解这些问题。
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