CARMA Causal Adaptive Remediation for Microservices — An Online, Predictive, Closed-Loop Approach to Performance Degradation

11th International Scientific Conference Technics, Informatics and Education – TIE 2026, (pp. 177-184)

 

AUTOR(I) / AUTHOR(S): Darko Radulović and Marija Blagojević       

  

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DOI: https://doi.org/10.46793/TIE26.177R

SAŽETAK /ABSTRACT:

Performance degradation in microservice systems propagates along service dependencies and surfaces as an SLO violation far from its true cause. Existing tools either diagnose causes offline or act without an explicit causal model; none combine online causal discovery, predictive propagation, and causal-driven remediation in one loop. This paper presents CARMA, which closes this gap. CARMA maintains an online causal graph over streaming per-service metrics via incremental neural-Granger discovery, predicts how a disturbance propagates to anticipate SLO violations, and ranks remediation actions by predicted causal effect within the MAPE-K loop. CARMA is implemented as a Kubernetes controller and evaluated on DeathStarBench Hotel Reservation under controlled fault injection, against correlation-, Granger-, and PC-based RCA. Across 50 trials, CARMA detects every resource- and availability-class fault (CPU stress, memory pressure, sustained pod failure) with 100% detection rate and localizes the true root cause in 84–99% of fault-window observations, versus 0–16% for baselines, with median lead 6–19 s. This round evaluated CARMA in diagnosis-only mode — actions were ranked and logged, not enacted — so closed-loop outcomes remain future work. Transient pod kills and network-latency injection are noted as current limitations. CARMA shows online causal reasoning localizes root causes more accurately than non-causal baselines.

KLJUČNE REČI / KEYWORDS: 

microservices; causal inference; self-adaptation; root cause analysis; performance degradation

 PROJEKAT / ACKNOWLEDGEMENT:

This study was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, and these results are parts of the Grant No. 451-03-34/2026-03/200132, with University of Kragujevac – Faculty of Technical Sciences Čačak.

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