SynergyRCA uses GPT-4o and a graph database of Kubernetes entity states to identify root causes of cluster incidents, reporting about 90 percent precision on two production clusters.
Mutiny! How does Kubernetes fail, and what can we do about it?
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this paper, we i) analyze and classify real-world failures of Kubernetes (the most popular container orchestration system), ii) develop a framework to perform a fault/error injection campaign targeting the data store preserving the cluster state, and iii) compare results of our fault/error injection experiments with real-world failures, showing that our fault/error injections can recreate many real-world failure patterns. The paper aims to address the lack of studies on systematic analyses of Kubernetes failures to date. Our results show that even a single fault/error (e.g., a bit-flip) in the data stored can propagate, causing cluster-wide failures (3% of injections), service networking issues (4%), and service under/overprovisioning (24%). Errors in the fields tracking dependencies between object caused 51% of such cluster-wide failures. We argue that controlled fault/error injection-based testing should be employed to proactively assess Kubernetes' resiliency and guide the design of failure mitigation strategies.
citation-role summary
citation-polarity summary
fields
cs.DC 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM
SynergyRCA uses GPT-4o and a graph database of Kubernetes entity states to identify root causes of cluster incidents, reporting about 90 percent precision on two production clusters.