Self-Adaptive Server Anomaly Detection Using Ensemble Meta-Reinforcement Learning

As the user’s behavior changes at any time with cloud computing and network services, abnormal server resource utilization traffic will lead to severe service crashes and system downtime. The traditional single anomaly detection model cannot handle the rapid failure prediction ahead. Therefore, this...

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Bibliographic Details
Published in:Electronics (Basel) 2024-06, Vol.13 (12), p.2348
Main Authors: Chang, Bao Rong, Tsai, Hsiu-Fen, Chen, Guan-Ru
Format: Article
Language:English
Subjects:
Accuracy
Adaptability
Algorithms
Anomalies
Cloud computing
Ensemble learning
File servers
Forecasts and trends
Machine learning
Manufacturing
Markov chains
Markov processes
Resource utilization
Servers
Time series
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Summary:As the user’s behavior changes at any time with cloud computing and network services, abnormal server resource utilization traffic will lead to severe service crashes and system downtime. The traditional single anomaly detection model cannot handle the rapid failure prediction ahead. Therefore, this study proposed ensemble learning combined with model-agnostic meta-reinforcement learning called ensemble meta-reinforcement learning (EMRL) to implement self-adaptive server anomaly detection rapidly and precisely, according to the time series of server resource utilization. The proposed ensemble approach combines hidden Markov model (HMM), variational autoencoder (VAE), temporal convolutional autoencoder (TCN-AE), and bidirectional long short-term memory (BLSTM). The EMRL algorithm trains this combination with several tasks to learn the implicit representation of various anomalous traffic, where each task executes trust region policy optimization (TRPO) to quickly adapt the time-varying data distribution and make rapid decisions precisely for an agent response. As a result, our proposed approach can improve the precision of anomaly prediction by 2.4 times and reduce the model deployment speed by 5.8 times on average because a meta-learner can immediately be applied to new tasks.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13122348