A Load Classification Method Running on Virtual Machines in Cloud Computing Environment

Abstract

The invention discloses a method for classifying operated load in a virtual machine under cloud computing environment. The method includes firstly acquiring monitoring parameters in 5 minutes of load operation and subjecting the monitored parameters to normalization processing; classifying load monitored into four categories such as CPU (central processing unit)-intensive load, memory intensive load, I / O (input / output) intensive load and network intensive load by means of TSRSVM (training sets refresh SVM); providing corresponding customized optimizing strategies for operation systems which running the four categories of intensive loads, and monitoring operating state of the systems through a performance comparison device; indicating that classification strategies are correct if performance of the systems is improved, otherwise, indicating that the classification strategies are incorrect. By the method, accuracy of load classification is high and system performance loss is low.

Description

technical field [0001] The invention relates to a load classification method running on a virtual machine in a cloud computing environment. Background technique [0002] With the rapid development of cloud computing technology represented by virtualization technology, more and more enterprises begin to use cloud computing system to improve system operation efficiency and management efficiency. The cloud computing platform shields virtual machines from the underlying hardware details, and different types of virtual machines can run on physical servers in the cloud system at the same time. At the same time, cloud computing also has the characteristics of dynamic contraction of virtual machine supply and efficient integration of server resources. This makes cloud computing a research hotspot. In the cloud computing environment, resources are obtained on demand, and the traditional virtual machine operating system achieves global adaptability to all types of applications, resul...

AI Technical Summary

Problems solved by technology

First of all, traditional decision tree classification, Bayesian classification, neural network algorithm, KNN and other machine learning classification algorithms are sensitive to the data dimension of the training set and test set. When the monitoring data dimension reaches 21 dimensions, the calculation time It is very long and consumes a lot of resources, which does not meet the requirements of the system

The SVM (SupportVectorMachine) classification method is not sensitive to dimensions, but the accuracy of classification in this environment is not good, and the requirements for the training set are too strict

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