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Anomaly Detection
TS-InvarNet: Anomaly Detection and Localization based on Tempo-spatial KPI Invariants in Distributed Services
In this paper, we design and implement TS-InvarNet, an interpretable end-to-end anomaly detection and diagnosis framework based on tempo-spatial KPI invariants.
Zijun Hu
,
Pengfei Chen
,
Guangba Yu
,
Zilong He
,
Xiaoyun Li
SwissLog: Robust Anomaly Detection andLocalization for Interleaved Unstructured Logs
In this paper, we propose SwissLog, namely a robust and unified deep learning based anomaly detection model for detecting diverse faults based on logs.
Xiaoyun Li
,
Pengfei Chen
,
Linxiao Jing
,
Zilong He
,
Guangba Yu
SwissLog: Robust and Unified Deep Learning Based Log Anomaly Detection for Diverse Faults
In this paper, we propose SwissLog, namely a robust and unified deep learning based anomaly detection model for detecting diverse faults based on logs.
Xiaoyun Li
,
Pengfei Chen
,
Linxiao Jing
,
Zilong He
,
Guangba Yu
A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems
In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data.
Zilong He
,
Pengfei Chen
,
Xiaoyun Li
,
Yongfeng Wang
,
Guangba Yu
,
Cailin Chen
,
Xinrui Li
,
Zibin Zheng
A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems
In this paper, we build a system named “VWR”, a framework of Virtual War Room for operating microservice applications which allows users to simulate their microservice architectures with low overhead and inject multiple types of faults into the microservice system with chaos engineering.
Hongyang Chen
,
Pengfei Chen
,
Guangba Yu
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