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Cited by 62Software System Performance and ReliabilityCloud Computing and Resource ManagementAnomaly Detection Techniques and Applications

Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework

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He, Hongxia, Li, Xi, Chen, Peng, Chen, Juan, Liu, Ming, Wu, Lei·2024·Journal of Cloud Computing
DOI 10.1186/s13677-024-00677-x
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Abstract

Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.

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  • Table 3 shows that some models such as [76,77,104,106] have adopted the principles of the dynamic graph approach to construct graphs at runtimes based on the dynamics represented in the MTSs.

    “Table 3 also shows that some models such as [76,77,104,106] have adopted the principles of the dynamic graph approach to endow the DNN models with the ability to construct the graphs at runtimes based on the dynamics represented in the MTSs.”

    From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [76]

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Cited by

Sorted by citations

Authors
He, H., Li, X., Chen, P., Chen, J., Liu, M., & Wu, L.
Affiliations
  • Xihua University (CN)
  • University of Electronic Science and Technology of China (CN)
Corresponding author
Chen, Peng
ORCID
Venue
Journal of Cloud Computing
Publisher
Springer Science and Business Media LLC
Published
2024-06-04
Volume
13
Issue
1
ISSN
2192-113X (ISSN-L)
DOI
10.1186/s13677-024-00677-x
OpenAlex
W4399469388
Cited by
62
References
3
Citations by year
2026: 12 · 2025: 18 · 2024: 32
FWCI
10.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Cloud Computing Advances Systems and Applications (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • the National Natural Science Foundation under Grant (No.62376043)
  • Science and Technology Program of Sichuan Province under Grant (No.2020JDRC0067, No.2023JDRC0087 and No.24NSFTD0025)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Keywords
cloud-based monitoring, graph neural networks, adaptive adjacency matrix, spatio-temporal features, unlabeled data

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