MicroRAS: Automatic Recovery in the Absence of Historical Failure Data for Microservice Systems

Wu, Li; Tordsson, Johan; Acker, Alexander; Kao, Odej

doi:10.1109/ucc48980.2020.00041

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Group1 → Group2 and Group2 → Group1. We argue that edges between graph nodes can be inferred from information about the respective deployment itself as presented in [17]. Next, each edge is tagged with their respective edge type, as described above.…”

Section: Edge Type Conditionmentioning

confidence: 99%

Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies

Scheinert

Acker²,

Thamsen³

et al. 2021

2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence)

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Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur. Utilizing machine learning for identification and localization of anomalies in such systems supports human experts and enables fast mitigation. However, due to the various interdependencies of system components, anomalies do not only affect their origin but propagate through the distributed system. Taking this into account, we present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies and placement as edges to improve the identification and localization of anomalies. Given a series of metric KPIs, our method predicts the most likely system state -either normal or an anomaly class -and performs localization when an anomaly is detected. During our experiments, we simulate a distributed cloud application deployment and synthetically inject anomalies. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.

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Section: Edge Type Conditionmentioning

confidence: 99%