A Guided Approach Towards Complex Chaos Selection, Prioritisation and Injection

Q1 Computer Science

IEEE Cloud Computing Pub Date : 2022-07-01 DOI:10.1109/CLOUD55607.2022.00025

Ojaswa Sharma, Mudit Verma, Saumya Bhadauria, P. Jayachandran

引用次数: 0

Abstract

Though Chaos Engineering is a popular method to test reliability and performance assurance, available tools can only inject random or manually curated faults into a target system. Given the vast array of faults that can be injected, it is crucial to a.) intelligently pick the faults that can have tangible effects, b.) increase the test coverage, and c.) reduce the overall time needed to assess the reliability of a system under adverse conditions. To the effect, we are proposing to learn from past major outages and use genetic algorithm-based meta-heuristics to evolve complex fault injections.

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复杂混沌选择、优先排序和注入的导向方法

虽然混沌工程是一种测试可靠性和性能保证的流行方法，但可用的工具只能将随机或人工策划的故障注入目标系统。考虑到可以注入的大量故障，至关重要的是a.)智能地选择可能具有实际影响的故障，b.)增加测试覆盖率，以及c.)减少在不利条件下评估系统可靠性所需的总时间。为此，我们建议从过去的重大故障中学习，并使用基于遗传算法的元启发式方法来进化复杂的故障注入。

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

IEEE Cloud Computing Computer Science-Computer Networks and Communications

CiteScore

11.20

自引率

0.00%

发文量

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