A virtual machine energy consumption prediction method

Abstract

The invention discloses a virtual machine energy consumption prediction method. The invention can realize the energy consumption prediction of the virtual machine. In this invention, by adding an acceleration term to the existing incremental limit learning machine model, the training error and compression factor of the network are fed back to the output of the hidden layer, so that the predictionresult is closer to the output sample, the number of redundant hidden layer nodes of the incremental limit learning machine can be reduced, and the network convergence speed of the incremental limit learning machine can be accelerated. By introducing compression factor and evolutionary solution, in the training process, by the output weights generated randomly, the network training errors, compression factors, input samples, better hidden layer node parameters including input weights, threshold, output weights, network training errors are calculated, the network structure can be optimized, thestability of the network training process is improved, thereby effectively reducing the network training errors.

Description

technical field [0001] The invention relates to the technical field of cloud computing, in particular to a virtual machine energy consumption prediction method. Background technique [0002] With the rapid development of the Internet and cloud computing, many cloud data centers use cloud computing services to provide cloud services, that is, cloud service providers. Currently, cloud data centers that provide cloud services consume a large amount of energy every day, and energy consumption costs have become a problem that cannot be ignored by cloud service providers. Therefore, how to save energy and reduce energy consumption has become a key issue that cloud service providers need to solve urgently. In the cloud service model of infrastructure as a service (IaaS), accurately predicting the energy consumption of virtual machines (VMs) is of great importance for the formulation of scheduling strategies and migration and merging strategies for virtual machine scheduling betwee...

AI Technical Summary

Problems solved by technology

In the prior art, the prediction model based on the traditional incremental extreme learning machine has many redundant nodes that reduce the accuracy and efficiency of virtual machine energy consumption prediction, and the random generation of hidden layer node parameters affects the stability of the incremental extreme learning machine Therefore, it is very important to design an efficient prediction model for virtual machine energy consumption prediction

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