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Chaos Engineering: A Multi-Vocal Literature Review

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arxiv 2412.01416 v2 pith:J5BWEUPI submitted 2024-12-02 cs.SE

classification cs.SE
keywords chaosengineeringliteraturesystemsresearchacademicchallengesgrey
verification ladder T0 review T1 audit T2 compute T3 formal
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Organizations, particularly medium and large enterprises, typically rely heavily on complex, distributed systems to deliver critical services and products. However, the growing complexity of these systems poses challenges in ensuring service availability, performance, and reliability. Traditional resilience testing methods often fail to capture the intricate interactions and failure modes of modern systems. Chaos Engineering addresses these challenges by proactively testing how systems in production behave under turbulent conditions, allowing developers to uncover and resolve potential issues before they escalate into outages. Though chaos engineering has received growing attention from researchers and practitioners alike, we observed a lack of reviews that synthesize insights from both academic and grey literature. Hence, we conducted a Multivocal Literature Review (MLR) on chaos engineering to address this research gap by systematically analyzing 96 academic and grey literature sources published between January 2016 and April 2024. We first used the chosen sources to derive a unified definition of chaos engineering and to identify key functionalities, components, and adoption drivers. We also developed a taxonomy for chaos engineering platforms and compared the relevant tools using it. Finally, we analyzed the current state of chaos engineering research and identified several open research issues.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML

    cs.PF 2025-07 conditional novelty 6.0 of 10

    Ecoscape provides a configurable chaos-injection benchmark with a weighted SLO violation score for comparing Kubernetes remediation strategies in edge ML inference.

  2. Resilience Evaluation of Kubernetes in Cloud-Edge Environments via Failure Injection

    cs.DC 2025-07 reject novelty 5.0 of 10

    Under network delay and partition faults, cloud-edge Kubernetes deployments show tighter response-time distributions than cloud-only deployments, while cloud deployments stay more stable under bandwidth throttling and...

  3. Designing a Custom Chaos Engineering Framework for Enhanced System Resilience at Softtech

    cs.SE 2025-06 conditional novelty 3.0 of 10

    The paper proposes a compliance-aware, four-phase chaos engineering framework for Softtech using LitmusChaos and a standard monitoring stack, but provides no implementation or validation.

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