一句话：提出用多目标搜索自动寻找由 RPC 微小性能退化组成的小组合，以有效注入分布式系统性能故障。
现代分布式系统的一次请求可能涉及十到数千次远程过程调用（RPC），其中任一 RPC 都可能引发性能退化。该博士研究计划针对生产环境中难以有效注入性能故障的问题，提出自动化工具与基于多目标搜索的技术，自动寻找由特定 RPC 引起的、微小但组合后对用户可感知性能有显著影响的性能退化事件。其目标是改进在生产中注入性能问题的能力，迫使开发者提前预见并缓解潜在退化。摘要未报告已完成的实验评估或具体结果，属于研究设想。
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.
DOI 原文 ·
@inproceedings{paperbot4764,
title = {A multi-objective framework for effective performance fault injection in distributed systems},
author = {Luca Traini},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3241535}
}