A Framework for Automatic Failure Recovery in ICT Systems by Deep Reinforcement Learning

Ikeuchi, Hiroki; Ge, Jiawen; Matsuo, Yoichi; Watanabe, Keishiro

doi:10.1109/icdcs47774.2020.00170

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Hernández-Serrato et al [30] discussed the possibilities to apply machine learning techniques for improving chaos engineering experiments. Ikeuchi et al [32] proposed a framework for learning a recovery policy using deep reinforcement learning and chaos engineering techniques. Chaos engineering is also applicable in the field of security.…”

Section: B Chaos Engineeringmentioning

confidence: 99%

Chaos Engineering of Ethereum Blockchain Clients

Zhang¹,

Ron²,

Baudry³

et al. 2021

Preprint

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The Ethereum blockchain is the operational backbone of major decentralized finance platforms. As such, it is expected to be exceptionally reliable. In this paper, we present CHAOSETH, a chaos engineering tool for resilience assessment of Ethereum clients. CHAOSETH operates in the following manner: First, it monitors Ethereum clients to determine their normal behavior. Then, it injects system call invocation errors into the Ethereum clients and observes the resulting behavior under perturbation. Finally, CHAOSETH compares the behavior recorded before, during, and after perturbation to assess the impact of the injected system call invocation errors. The experiments are performed on the two most popular Ethereum client implementations: GoEthereum and OpenEthereum. We experiment with 22 different types of system call invocation errors. We assess their impact on the Ethereum clients with respect to 15 applicationlevel metrics. Our results reveal a broad spectrum of resilience characteristics of Ethereum clients in the presence of system call invocation errors, ranging from direct crashes to full resilience. The experiments clearly demonstrate the feasibility of applying chaos engineering principles to blockchains.

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Section: B Chaos Engineeringmentioning

confidence: 99%