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TrinityRCL : Multi-granular and code-level root cause localization using multiple types of telemetry data in microservice systems
Gu, Shenghui Rong, Guoping Ren, Tian Zhang, He Shen, Haifeng Yu, Yongda Li, Xian Ouyang, Jian Chen, Chunan
Gu, Shenghui
Rong, Guoping
Ren, Tian
Zhang, He
Shen, Haifeng
Yu, Yongda
Li, Xian
Ouyang, Jian
Chen, Chunan
Abstract
The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to provide stable and continued services. However, compared with monolithic systems, software systems based on the layered microservice architecture are inherently complex and commonly involve entities at different levels of granularity. Therefore, for effective service monitoring, these systems have a special requirement of multi-granular RCA. Furthermore, as a large proportion of anomalies in microservice systems pertain to problematic code, to timely troubleshoot these anomalies, these systems have another special requirement of RCA at the finest code-level. Microservice systems rely on telemetry data to perform service monitoring and RCA of service anomalies. The majority of existing RCA approaches are only based on a single type of telemetry data and as a result can only support uni-granular RCA at either application-level or service-level. Although there are attempts to combine metric and tracing data in RCA, their objective is to improve RCA's efficiency or accuracy rather than to support multi-granular RCA. In this article, we propose a new RCA solution TrinityRCL that is able to localize the root causes of anomalies at multiple levels of granularity including application-level, service-level, host-level, and metric-level, with the unique capability of code-level localization by harnessing all three types of telemetry data to construct a causal graph representing the intricate, dynamic, and nondeterministic relationships among the various entities related to the anomalies. By implementing and deploying TrinityRCL in a real production environment, we evaluate TrinityRCL against two baseline methods and the results show that TrinityRCL has a significant performance advantage in terms of accuracy at the same level of granularity with comparable efficiency and is particularly effective to support large-scale systems with massive telemetry data.
Keywords
computer network security, graph theory, service oriented architecture, telemetry, application level, code level localization, code level root cause localization, continued services, effective service monitoring, finest code level, host level, large scale software systems, large scale systems, layered microservice architecture, massive telemetry data, metric level, microservice systems, monolithic systems, multigranular rca, online services, rca solution trinity rc lthat, service anomalies, service level, stable services, unigranular rca, microservice architectures, telemetry, measurement, codes, monitoring, location awareness, computer architecture, root cause, telemetry data, microservices
Date
2023
Type
Journal article
Journal
IEEE Transactions on Software Engineering
Book
Volume
49
Issue
5
Page Range
3071-3088
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled