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Register a new userGroot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings
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Added by
Hanzhang Wang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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For large-scale distributed systems, its crucial to efficiently diagnose the root causes of incidents to maintain high system availability. The recent development of microservice architecture brings three major challenges (i.e., operation, system scale, and monitoring complexities) to root cause analysis (RCA) in industrial settings. To tackle these challenges, in this paper, we present Groot, an event-graph-based approach for RCA. Groot constructs a real-time causality graph based on events that summarize various types of metrics, logs, and activities in the system under analysis. Moreover, to incorporate domain knowledge from site reliability engineering (SRE) engineers, Groot can be customized with user-defined events and domain-specific rules. Currently, Groot supports RCA among 5,000 real production services and is actively used by the SRE teamin a global e-commerce system serving more than 185 million active buyers per year. Over 15 months, we collect a data setcontaining labeled root causes of 952 real production incidents for evaluation. The evaluation results show that Groot is able to achieve 95% top-3 accuracy and 78% top-1 accuracy. To share our experience in deploying and adopting RCA in industrial settings, we conduct survey to show that users of Grootfindit helpful and easy to use. We also share the lessons learnedfrom deploying and adopting Grootto solve RCA problems inproduction environments.
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We describe a formal approach to identify root causes of outliers observed in $n$ variables $X_1,dots,X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG). To this end, we first introduce a systematic way to define outlier scores. Further, we introduce the concept of conditional outlier score which measures whether a value of some variable is unexpected *given the value of its parents* in the DAG, if one were to assume that the causal structure and the corresponding conditional distributions are also valid for the anomaly. Finally, we quantify to what extent the high outlier score of some target variable can be attributed to outliers of its ancestors. This quantification is defined via Shapley values from cooperative game theory.
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This paper reports on the results of the French ANR IMPEX research project dealing with making explicit domain knowledge in design models. Ontologies are formalised as theories with sets, axioms, theorems and reasoning rules. They are integrated to design models through an annotation mechanism. Event-B has been chosen as the ground formal modelling technique for all our developments. In this paper, we particularly describe how ontologies are formalised as Event-B theories.
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