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Intelligent Evaluation Method for Cloud Platform Operation and Maintenance Based on Artificial Neural Network

Published: 17 January 2024 Publication History

Abstract

The continuous growth of cloud computing services has opened a new era of technological advancement, changing the way businesses operate and users access services. With the increasing scale and complexity of cloud infrastructure, efficient management and maintenance of the cloud platform have become more prominent responsibilities. Considering the complex interdependencies and dynamic nature of the cloud environment, traditional manual methods of operating a cloud platform have proven to be insufficient. Therefore, there is an urgent need to develop intelligent approaches that can automatically assess and optimize cloud platform operations. To address these challenges, this paper introduces a new approach using artificial neural networks for intelligent assessment of cloud platform maintenance. By leveraging the power of artificial neural networks, comprehensive analysis and learning from historical operational data can be conducted, enabling accurate performance prediction and evaluation. This paper presents a neural network-based approach for assessing cloud platform maintenance using artificial intelligence techniques. In response to the issues existing in the backpropagation (BP) network, this paper improves the weight decay strategy of the particle swarm optimization (PSO) algorithm and constructs the improved particle swarm optimization (IPSO). Then, IPSO is used to optimize the BP network, resulting in the IPSO-BP approach. Finally, experimental results demonstrate that the proposed strategy is feasible and effective.

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PCCNT '23: Proceedings of the 2023 International Conference on Power, Communication, Computing and Networking Technologies
September 2023
552 pages
ISBN:9781450399951
DOI:10.1145/3630138
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2024

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