11th International Scientific Conference Technics, Informatics and Education – TIE 2026, (pp. 170-176)
AUTOR(I) / AUTHOR(S): Darko Radulović
and Marija Blagojević
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DOI: https://doi.org/10.46793/TIE26.170R
SAŽETAK /ABSTRACT:
Modern cloud applications are built as graphs of loosely coupled microservices, where a localized disturbance – a CPU spike, a slow dependency, a saturated queue – can propagate and cause performance degradation in seemingly unrelated services. Two research communities address parts of this problem. Causal root-cause analysis (RCA) explains why an incident happened, but most methods operate offline and stop at diagnosis. Self-adaptive systems act to restore service levels, but their decisions rely on thresholds, service-level objectives, or black-box reinforcement learning rather than explicit cause-effect knowledge. This paper reviews 40 works across causal RCA, self-adaptive systems, and causal discovery, and organizes them along five dimensions: whether they are online, causal, predictive, closed-loop, and how they are validated. A comparative analysis of 19 representative approaches shows that no existing method combines online causal-graph discovery, predictive degradation propagation, and causal-driven closed-loop remediation. This intersection is defined as an open problem, and a conceptual framework that extends the MAPE-K self-adaptation loop with a streaming causal layer is proposed. Four research questions and a validation plan against RCA and self-adaptation baselines are stated.
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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