ORECA: Evaluating cadence and elasticity impacts on root cause analysis in edge-cloud continuum

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master_Nguyen_Dung_2026.pdf (736.77 KB)

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School of Science | Master's thesis
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Date

2025-12-31

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Major/Subject

Computer Science

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Degree programme

Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences

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en

Pages

63

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Abstract

End-to-end microservice pipelines are increasingly deployed across the edge-cloud continuum, which introduces substantial heterogeneity in resource capabilities for highly elastic workloads. As service dependencies span across edge and cloud computing nodes, identifying the root cause of system and application faults becomes increasingly challenging. For the developer and application provider to consider suitable root cause analysis (RCA) techniques for their diverse edge-cloud fault scenarios, evaluation frameworks are needed to systematically assess their strengths and limitations across diverse operational conditions. Existing RCA evaluation frameworks allow customization of techniques and evaluation metrics, but they neglect configuration aspects related to fault scenarios, particularly those involving elasticity, fault severity, and observability cadences in edge-cloud continuum. In this thesis, we propose a holistic scenario-based RCA evaluation workflow, encompassing both fault scenario generation and RCA method assessment. With this foundation, we implement ORECA for evaluating RCA under diverse runtime behaviors, observability configurations, application models, and edge-cloud environments. ORECA provides fault scenario specifications and integrates observability tools along with chaos engineering to allow customized, scenario-based end-to-end RCA evaluation workflows, covering scenario setup, dataset creation, and performance evaluation. We use ORECA to evaluate the current state-of-the-art RCA algorithms for ML-intensive applications in edge-cloud continuum under various observability cadences, fault severities, and elasticity behaviors. Based on these findings, we provide recommendations for further development of RCA algorithms for microservice-based pipelines from various perspectives.

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Supervisor

Truong, Linh

Thesis advisor

Nguyen, Hong-Tri

Keywords

root cause analysis, edge-cloud continuum, benchmark, microservices, AI/ML pipelines, RCA evaluation

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https://urn.fi/URN:NBN:fi:aalto-202601221896

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[dipl] Perustieteiden korkeakoulu / SCI