Bo Cui
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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Index Terms
- Intelligent Evaluation Method for Cloud Platform Operation and Maintenance Based on Artificial Neural Network
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