Open Access
ARTICLE
Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning
Mingguang Yu1,2, Xia Zhang1,2,*
1 School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China
2 Neusoft Corporation, Shenyang, 110179, China
* Corresponding Author: Xia Zhang. Email:
Intelligent Automation & Soft Computing 2023, 37(2), 1787-1806. https://doi.org/10.32604/iasc.2023.038798
Received 29 December 2022; Accepted 18 April 2023; Issue published 21 June 2023
Abstract
As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly
complex. This complexity makes it difficult to detect anomalies in cloud
systems. The system status can no longer be determined through individual
key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies
in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available
state. Therefore, accurately modeling cloud systems and mining the hidden
system state is crucial. To address this challenge, we propose an anomaly
detection method with dynamic spatiotemporal learning (AD-DSTL). ADDSTL leverages the spatiotemporal dynamics of the system to train an endto-end deep learning model driven by data from system monitoring to detect
underlying anomalous states in complex cloud systems. Unlike previous work
that focuses on the KPIs of separate components, AD-DSTL builds a model
for the entire system and characterizes its spatiotemporal dynamics based on
graph convolutional networks (GCN) and long short-term memory (LSTM).
We validated AD-DSTL using four datasets from different backgrounds, and
it demonstrated superior robustness compared to other baseline algorithms.
Moreover, when raising the target exception level, both the recall and precision
of AD-DSTL reached approximately 0.9. Our experimental results demonstrate that AD-DSTL can meet the requirements of anomaly detection for
complex cloud systems.
Keywords
Cite This Article
APA Style
Yu, M., Zhang, X. (2023). Anomaly detection for cloud systems with dynamic spatiotemporal learning. Intelligent Automation & Soft Computing, 37(2), 1787-1806. https://doi.org/10.32604/iasc.2023.038798
Vancouver Style
Yu M, Zhang X. Anomaly detection for cloud systems with dynamic spatiotemporal learning. Intell Automat Soft Comput . 2023;37(2):1787-1806 https://doi.org/10.32604/iasc.2023.038798
IEEE Style
M. Yu and X. Zhang, "Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning," Intell. Automat. Soft Comput. , vol. 37, no. 2, pp. 1787-1806. 2023. https://doi.org/10.32604/iasc.2023.038798