FaultSee: Reproducible Fault Injection in Distributed Systems

Miguel Amaral, M. Pardal, H. Mercier, M. Matos
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引用次数: 1

Abstract

Distributed systems are increasingly important in modern society, often operating on a global scale with stringent dependability requirements. Despite the vast amount of research and the development of techniques to build dependable systems, faults are inevitable as one can witness from regular failures of major providers of IT services. It is therefore fundamental to evaluate distributed systems under different fault patterns and adversarial conditions to assess their high-level behaviour and minimize the occurrence of failures. However, succinctly capturing the system configuration, environment, fault patterns and other variables affecting an experiment is very hard, leading to a reproducibility crisis. In this paper we propose the FaultSee toolkit. The two components of FaultSee are (1) the simple and descriptive FDSL language that captures the system, environment, workload and fault pattern characteristics; and (2) an easy-to-use platform to deploy and run the experiments described by the language. FaultSee allows to precisely describe and reproduce experiments and leads to a better assessment the impact of faults in distributed systems. We showcase the key features of FaultSee by studying the impact of faults with real deployments of Apache Cassandra and BFT-Smart.
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参见:分布式系统中可再现的故障注入
分布式系统在现代社会中越来越重要,通常在全球范围内运行,具有严格的可靠性要求。尽管对构建可靠系统的技术进行了大量的研究和开发,但从主要IT服务提供商的常规故障中可以看出,故障是不可避免的。因此,在不同的故障模式和对抗条件下评估分布式系统以评估其高级行为并最大限度地减少故障的发生是至关重要的。然而,简洁地捕捉系统配置、环境、故障模式和其他影响实验的变量是非常困难的,这会导致再现性危机。在本文中,我们提出了FaultSee工具包。FaultSee的两个组成部分是:(1)捕获系统、环境、工作负载和故障模式特征的简单描述性FDSL语言;(2)一个易于使用的平台来部署和运行该语言描述的实验。FaultSee允许精确地描述和重现实验,并导致更好地评估分布式系统中故障的影响。通过在Apache Cassandra和BFT-Smart的实际部署中研究故障的影响,我们展示了FaultSee的关键特性。
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