Energy-optimized virtual machine deployment method and system

Abstract

The invention discloses an energy-optimized virtual machine deployment method and system. The method comprises the following steps: step 1, determining a virtual machine deployment target and constraint conditions thereof; 2, determining deployment solution calculation initialization parameters; step 3, population initialization is carried out, so that the dolio-sea squirt individuals are in one-to-one correspondence with deployment solutions of the virtual machines; 4, according to the virtual machine deployment target and the constraint conditions of the virtual machine deployment target, calculating the fitness of each scabbard individual in the initialized population; 5, determining a food source, a leader and a follower to obtain an updated population; 6, calculating and updating thefitness of each scabbard individual in the population, and determining a food source; and 7, repeating the steps 5-6, and outputting the food source position in the current population as a final virtual machine deployment optimal solution. According to the method, exploration behaviors and development behaviors in the iteration process are balanced through the group optimization model of the Sashimi, and the virtual machine deployment effect of energy optimization is achieved while the globality and diversity are guaranteed.

Description

technical field [0001] The present invention relates to the field of cloud computing and intelligent swarm algorithm, and more specifically, to an energy-optimized virtual machine deployment method and system. Background technique [0002] Cloud computing can overcome the deficiencies of traditional computing models and localized computing methods for data center owners through its flexibility, security, high scalability, and quality of service guarantees. It can be configured with a large number of heterogeneous servers of different types, and a large number of virtual machines can be further deployed on the server by using virtualization technology, which is beneficial to the isolation of applications when virtual machines provide services. Virtual machines also have different load types and different requirements for resources of each dimension, which may lead to conflicts between resource utilization and high energy consumption of hosts. The high energy consumption of t...

AI Technical Summary

Problems solved by technology

[0003] Intelligent swarm algorithm is a typical algorithm for solving optimization problems. Among them, particle swarm algorithm, genetic algorithm, ant colony algorithm, bee colony algorithm and gravitational search algorithm have all been applied in the virtual machine deployment problem in the cloud computing environment. As one of the optimization goals, the energy consumption problem has achieved many phased results, but due to the inherent limitations of this type of meta-heuristic algorithm, such as falling into local optimum and relying on too many initial parameters, etc., the virtual machine deployment optimization based on energy consumption The problem already has certain limitations, and a meta-heuristic algorithm with better performance is needed to optimize the solution to this problem

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