April 30, 2024Journal of Engineering and Applied ScienceOpen Access

Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines

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GJGuoming JiangZhejiang Sci-Tech University

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Computational evaluation demonstrates up to 99.8% anomaly detection accuracy in cloud virtual machines, highlighting reduced maintenance demands for infrastructure platforms.

Key Points

An adaptive anomaly detection model achieves 99.8% classification accuracy for infrastructure-as-a-service cloud virtual machines, improving operational dependability.
Detection accuracy reaches 96.7% with a 2-kernel Zoom layer, while deep Q-network algorithms maintain an overall accuracy range of 96.7% to 98.6% across monitoring metrics.
Assessment of virtual machine monitoring metrics using migration learning principles reduces operational workloads, supporting reliable cloud computing architectures.

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Guoming Jiang (2024) studied this question.

synapsesocial.com/papers/68e6cc04b6db64358764a461 https://doi.org/10.1186/s44147-024-00439-1

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Computational evaluation demonstrates up to 99.8% anomaly detection accuracy in cloud virtual machines, highlighting reduced maintenance demands for infrastructure platforms.

Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines

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Member takes

Implication

Key Points

Cite This Study

Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines

Discussion

Member takes

Implication

Key Points

Cite This Study

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