Server performance prediction method based on particle swarm optimization nerve network

A particle swarm optimization and neural network technology, applied in the field of computer performance management, can solve problems such as loss of speed, inactivity, and difficulty in finding a global optimal solution, and achieve the effect of improving convergence and accuracy.

Inactive Publication Date: 2013-06-19
NANJING UNIV OF POSTS & TELECOMM
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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 parti

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  • Server performance prediction method based on particle swarm optimization nerve network
  • Server performance prediction method based on particle swarm optimization nerve network
  • Server performance prediction method based on particle swarm optimization nerve network

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[0042] The technical solution of the present invention will be described in detail below in conjunction with the drawings:

[0043] The present invention improves the particle swarm optimization algorithm and proposes a particle adjustment method based on the distribution of particle swarms. The main idea is to improve the particle swarm optimization algorithm in the existing particle swarm optimization neural network. In the process, when the particle swarm is densely distributed, add a random position increment to break up the particles, thereby jumping out of the local optimal solution.

[0044] In order to facilitate the public to understand the technical solution of the present invention, the following takes the PSO-Elman neural network prediction model as an example for detailed description.

[0045] Such as figure 1 As shown, the Elman neural network structure includes four layers: input layer, hidden layer, structure layer, and output layer. Elman neural network feeds back t...

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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...

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Application Information

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IPC IPC(8): G06N3/08
Inventor 程春玲李阳张登银张怡婷万腾
Owner NANJING UNIV OF POSTS & TELECOMM
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