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Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Software and Systems Engineering Group, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
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Electronics 2020, 9(1), 64; https://doi.org/10.3390/electronics9010064
Received: 6 November 2019 / Revised: 3 December 2019 / Accepted: 12 December 2019 / Published: 1 January 2020
(This article belongs to the Section Computer Science & Engineering)
Detecting the location of performance anomalies in complex distributed systems is critical to ensuring the effective operation of a system, in particular, if short-lived container deployments are considered, adding challenges to anomaly detection and localization. In this paper, we present a framework for monitoring, detecting and localizing performance anomalies for container-based clusters using the hierarchical hidden Markov model (HHMM). The model aims at detecting and localizing the root cause of anomalies at runtime in order to maximize the system availability and performance. The model detects response time variations in containers and their hosting cluster nodes based on their resource utilization and tracks the root causes of variations. To evaluate the proposed framework, experiments were conducted for container orchestration, with different performance metrics being used. The results show that HHMMs are able to accurately detect and localize performance anomalies in a timely fashion. View Full-Text
Keywords: anomaly detection; anomaly identification; anomaly injection; cloud; containers; cluster; hierarchical hidden Markov model anomaly detection; anomaly identification; anomaly injection; cloud; containers; cluster; hierarchical hidden Markov model
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MDPI and ACS Style

Samir, A.; Pahl, C. Detecting and Localizing Anomalies in Container Clusters Using Markov Models. Electronics 2020, 9, 64. https://doi.org/10.3390/electronics9010064

AMA Style

Samir A, Pahl C. Detecting and Localizing Anomalies in Container Clusters Using Markov Models. Electronics. 2020; 9(1):64. https://doi.org/10.3390/electronics9010064

Chicago/Turabian Style

Samir, Areeg, and Claus Pahl. 2020. "Detecting and Localizing Anomalies in Container Clusters Using Markov Models" Electronics 9, no. 1: 64. https://doi.org/10.3390/electronics9010064

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