TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems

Author:

Zeng Zhengran1,Zhang Yuqun1,Xu Yong1,Ma Minghua1,Qiao Bo1,Zou Wentao2,Chen Qingjun2,Zhang Meng2,Zhang Xu1,Zhang Hongyu3,Gao Xuedong2,Fan Hao2,Rajmohan Saravan2,Lin Qingwei1,Zhang Dongmei1

Affiliation:

1. Southern University of Science and Technology

2. Microsoft 365

3. Chongqing University

Publisher

IEEE

Reference45 articles.

1. Anomaly Detection from System Tracing Data Using Multimodal Deep Learning

2. Suod: Accelerating large-scale unsupervised heterogeneous outlier detection;zhao;Proceedings of Machine Learning and Systems,2021

3. Jaeger;Jaegermann,2022

4. LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

5. Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks

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