An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on  Q  Learning Mechanism

Shuguang Chen

doi:10.1155/2021/2119993

Abstract

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

Highlights

In recent years, the development scale of service computing model has gradually expanded and has been widely used in various fields
In order to solve the problems in the above algorithms, an adaptive infrastructure as a service (IaaS) cloud virtual machine deployment algorithm based on the Q learning mechanism is proposed
In order to verify the overall effectiveness of the adaptive deployment algorithm for IaaS cloud virtual machines based on the Q learning mechanism, it is necessary to conduct related tests on the proposed algorithm

Summary

Introduction

The development scale of service computing model has gradually expanded and has been widely used in various fields. Liao et al [5] combined dynamic programming method and Markov models to predict the price of virtual machines and deployed virtual machines in a cloud environment according to the execution time limit of the workflow. This algorithm could not simplify the virtual machine deployment process. In order to solve the problems in the above algorithms, an adaptive IaaS cloud virtual machine deployment algorithm based on the Q learning mechanism is proposed.

Related Works

Experiments and Results

Conclusion