Proactive & Fine-Grained Monitoring For Microservice Call Chains In Cloud-Native Applications Through Latency Distribution Prediction

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dc.contributor.advisorLitoiu, Marin
dc.contributor.authorHussain, Hamza
dc.date.accessioned2025-04-10T10:44:49Z
dc.date.available2025-04-10T10:44:49Z
dc.date.copyright2024-10-01
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:44:49Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractModern cloud-native applications are distributed in nature and have their health monitored through multiple channels. In this study, we propose a singular novel approach that leverages multi-channel monitoring data for fine-grained performance analysis, proactive anomaly prediction, and root-cause analysis in microservices based applications. To this end, we employ Microservice Embeddings, Graph Neural Networks (GNN), and Gated Recurrent Units (GRU) to predict latency distribution, as opposed to a single latency value, for individual calls within a microservice call chain, as well as the distribution of end-to-end latency. Thus, our approach enables deeper insights into system performance and targeted diagnostics for anomalies. We use several benchmark datasets containing anomalies and show that our approach performs consistently across the latency spectrum while outperforming baseline latency prediction approaches by about 6%. Lastly, we show that our approach can be efficiently used to automate the process of trace-based anomaly prediction and perform root-cause analysis.
dc.identifier.urihttps://hdl.handle.net/10315/42771
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsAnomaly Prediction
dc.subject.keywordsMicroservices
dc.subject.keywordsLatency prediction
dc.subject.keywordsMicroservice performance
dc.titleProactive & Fine-Grained Monitoring For Microservice Call Chains In Cloud-Native Applications Through Latency Distribution Prediction
dc.typeElectronic Thesis or Dissertation

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