Latent error prediction and fault localization for microservice applications by learning from system trace logs

Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of latent error prediction and fault localization for microservice applications by learning from system trace logs. Based on a set of features defined on the system trace logs, MEPFL trains prediction models at both the trace level and the microservice level using the system trace logs collected from automatic executions of the target application and its faulty versions produced by fault injection. The prediction models thus can be used in the production environment to predict latent errors, faulty microservices, and fault types for trace instances captured at runtime. We implement MEPFL based on the infrastructure systems of container orchestrator and service mesh, and conduct a series of experimental studies with two opensource microservice applications (one of them being the largest open-source microservice application to our best knowledge). The results indicate that MEPFL can achieve high accuracy in intraapplication prediction of latent errors, faulty microservices, and fault types, and outperforms a state-of-the-art approach of failure diagnosis for distributed systems. The results also show that MEPFL can effectively predict latent errors caused by real-world fault cases.

Original languageEnglish (US)
Title of host publicationESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
EditorsSven Apel, Marlon Dumas, Alessandra Russo, Dietmar Pfahl
PublisherAssociation for Computing Machinery, Inc
Pages683-694
Number of pages12
ISBN (Electronic)9781450355728
DOIs
StatePublished - Aug 12 2019
Event27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 - Tallinn, Estonia
Duration: Aug 26 2019Aug 30 2019

Publication series

NameESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Conference

Conference27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019
CountryEstonia
CityTallinn
Period8/26/198/30/19

Keywords

  • Debugging
  • Error prediction
  • Fault localization
  • Machine learning
  • Microservices
  • Tracing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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  • Cite this

    Zhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Liu, D., Xiang, Q., & He, C. (2019). Latent error prediction and fault localization for microservice applications by learning from system trace logs. In S. Apel, M. Dumas, A. Russo, & D. Pfahl (Eds.), ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 683-694). (ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering). Association for Computing Machinery, Inc. https://doi.org/10.1145/3338906.3338961