Server performance prediction method based on particle swarm optimization nerve network

Abstract

The invention discloses a server performance prediction method based on a particle swarm optimization nerve network, and belongs to the technical field of computer performance management. The performance of a server in cloud computing is predicted based on an improved Elman nerve network. Firstly, the number of nodes of an input layer of the Elman nerve network according to relevance of sample data; and secondly, the Elman nerve network is trained by a PSO (particle swarm optimization) algorithm based on particle swarm distribution. The concept of particle aggregation degree is introduced in the PSO algorithm based on particle swarm distribution, a particle swarm is scattered when the aggregation degree is high, diversity of the particle swarm is kept, and the optimizing capacity of the algorithm is improved. Fine precision of a prediction model in short-term prediction and long-term prediction is kept, and the training speed of the nerve network is increased.

Description

technical field [0001] The invention relates to a server performance prediction method, in particular to a server performance prediction method based on a particle swarm optimization neural network suitable for cloud computing, and belongs to the technical field of computer performance management. Background technique [0002] As the scale of the cloud platform becomes larger and larger, how to improve the resource utilization of servers in the cloud environment has become an important issue in cloud management. In resource scheduling, in order to adjust the increase and release of resources in time and avoid excessively frequent resource scheduling, it is necessary to predict the performance of servers in the cloud platform. Two requirements must be met in performance prediction: On the one hand, the accuracy of performance prediction must be high, otherwise it will have a significant impact on the resource scheduling of the cloud platform, and even cause the cloud platform...

AI Technical Summary

Problems solved by technology

However, the optimization ability of the standard particle swarm optimization algorithm mainly depends on the interaction between particles. During each iteration, the particles in the particle swarm continuously approach the optimal particle to the global optimal solution, and more and more particles will gather swarm, and lose their speed, become less and less active, hard to find the global optimal solution

This situation can seriously affect the accuracy of the forecast

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