Anomaly Detection from System Tracing Data Using Multimodal Deep Learning-Reference-Cited by-同舟云学术

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Anomaly Detection from System Tracing Data Using Multimodal Deep Learning

Published:2019-07 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE 12th International Conference on Cloud Computing (CLOUD)
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Author:

Nedelkoski Sasho,Cardoso Jorge,Kao Odej

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8798376/8814488/08814585.pdf?arnumber=8814585

Cited by 57 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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

2. TraStrainer: Adaptive Sampling for Distributed Traces with System Runtime State;Proceedings of the ACM on Software Engineering;2024-07-12

3. Trace Anomaly Detection for Microservice Systems via Graph-based Semi-supervised Learning;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

4. SampleHST-X: A Point and Collective Anomaly-Aware Trace Sampling Pipeline with Approximate Half Space Trees;Journal of Network and Systems Management;2024-04-16

5. Toward Adaptive Tracing: Efficient System Behavior Analysis using Language Models;Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results;2024-04-14

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