@Article{iasc.2023.038798,
AUTHOR = {Mingguang Yu, Xia Zhang},
TITLE = {Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {2},
PAGES = {1787--1806},
URL = {http://www.techscience.com/iasc/v37n2/53268},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2023.038798}
}