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TraceGra: A trace-based anomaly detection for microservice using graph deep learning

  • Published:2023-04 Issue: Volume:204 Page:109-117
  • ISSN:0140-3664
  • Container-title:Computer Communications
  • language:en
  • Short-container-title:Computer Communications

Author:

Chen Jian,Liu Fagui,Jiang JunORCID,Zhong Guoxiang,Xu Dishi,Tan Zhuanglun,Shi Shangsong

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference38 articles.

1. Towards an understanding of microservices;Shadija,2017

2. JCallGraph: tracing microservices in very large scale container cloud platforms;Liu,2019

3. Performance anomaly detection and bottleneck identification;Ibidunmoye;ACM Comput. Surv.,2015

4. Dapper, a large-scale distributed systems tracing infrastructure;Sigelman,2010

5. Dynamic incremental ensemble fuzzy classifier for data streams in green internet of things;Jiang;IEEE Trans. Green Commun. Netw.,2022

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1. MicroNet: Operation Aware Root Cause Identification of Microservice System Anomalies;IEEE Transactions on Network and Service Management;2024-08

2. Leveraging Large Language Models for the Auto-remediation of Microservice Applications: An Experimental Study;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

3. An effective failure detection method for microservice-based systems using distributed tracing data;Engineering Applications of Artificial Intelligence;2024-07

4. Enhancing fault localization in microservices systems through span-level using graph convolutional networks;Automated Software Engineering;2024-06-05

5. MAAD: A Distributed Anomaly Detection Architecture for Microservices Systems;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27
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