Virtual machine allocation method based on particle swarm optimization

Abstract

The invention relates to the technical field of cloud computing (IaaS), in particular to a virtual machine allocation method based on particle swarm optimization. The method includes the steps of obtaining the virtual machine request and physical host resource of a data center, and constructing a virtual machine list and a physical host list; initializing a particle swarm, and setting the parameters of the particle swarm optimization; calculating the fitness value of each particle in the particle swarm, and recording the historical optimal particles of individuals and the optimal particles of population according to the particle fitness value; updating the speed and location of each particle according to an update strategy; judging whether or not the maximum time number of iterations is satisfied, if yes, outputting global optimal particle code, and if no, continuing to iterate; decoding the global optimal particle code into a virtual machine allocation scheme, and outputting the scheme. The method can improve the resource utilization when reducing the response time, and meanwhile achieve a better balance between the load balance degree and energy consumption.

Description

technical field [0001] The invention relates to the technical field of cloud computing (IaaS), in particular to a virtual machine allocation method based on particle swarm optimization. Background technique [0002] As a new type of business service model, cloud computing is gradually becoming an important development direction of the computer industry, and has attracted more and more attention from researchers and the public. With the development of cloud technology and the sharp increase in the scale of data centers, how to improve resource utilization and reduce response time are important issues that cloud computing platform managers need to consider, and virtual machine deployment optimization is the solution to improving resource utilization. and one of the key issues in reducing response time. In the cloud computing mode, the user applies for a group of virtual machines from the data center and specifies the size of various resources requested by each virtual machine...

AI Technical Summary

Problems solved by technology

Yang Jing et al. proposed a multi-population Gaussian learning particle swarm optimization algorithm to solve the load balancing problem, but this algorithm does not consider the energy consumption optimization problem

The above method is applied to the virtual machine deployment optimization problem and has achieved certain results, but there are also problems such as poor convergence and sensitive parameters.

[0007] Most of the above research work focuses on energy consumption optimization, ignoring the problem of violating user service levels while optimizing resource utilization

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