TraceCRL: contrastive representation learning for microservice trace analysis

Author:

Zhang Chenxi1,Peng Xin1,Zhou Tong1,Sha Chaofeng1,Yan Zhenghui1,Chen Yiru1,Yang Hong1

Affiliation:

1. Fudan University, China

Publisher

ACM

Reference41 articles.

1. Automated Analysis of Distributed Tracing: Challenges and Research Directions

2. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey E. Hinton . 2020. A Simple Framework for Contrastive Learning of Visual Representations . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020. 119 , PMLR, 1597–1607. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020. 119, PMLR, 1597–1607.

3. elastic. 2022. Elasticsearch. https://www.elastic.co/elasticsearch/ elastic. 2022. Elasticsearch. https://www.elastic.co/elasticsearch/

4. node2vec

5. Graph-based trace analysis for microservice architecture understanding and problem diagnosis

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1. MicroCM: A cloud monitoring architecture for microservice invocation;Computer Networks;2024-01

2. Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

3. Efficient and Robust Trace Anomaly Detection for Large-Scale Microservice Systems;2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE);2023-10-09

4. BertHTLG: Graph-Based Microservice Anomaly Detection Through Sentence-Bert Enhancement;Web Information Systems and Applications;2023

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