An Optimal Method for Mapping Virtual Machines to Physical Machines

Abstract

The present invention discloses an optimization method for virtual machine to physical machine mapping, comprising the following steps: S1, modeling of virtual machine requirements; S2, determination of threshold; S3, construction of priority queue; S4, dequeue in batches; S5, optimization Core design; S6, algorithm parallelization. The optimization method for mapping a virtual machine to a physical machine in the invention improves the effectiveness of the algorithm and the characteristics of low time delay. The invention can timely and effectively determine the allocation position of the virtual machine according to the specific virtual machine requirement, and make targeted dynamic adjustments to the mapping of the virtual machine to the physical machine resource when the virtual machine requirement dynamically changes. During the application process, the utilization rate of cloud resources can be improved, and the operating cost of cloud service providers can be reduced.

Description

technical field [0001] The invention relates to a cloud resource scheduling algorithm, in particular to a cloud resource scheduling algorithm optimization method for mapping a virtual machine to a physical machine. Background technique [0002] At present, cloud resources are characterized by heterogeneity, uncertainty, and dynamics, and are sensitive to delay requirements in the scheduling process. These unique properties of cloud resources increase the difficulty of cloud resource scheduling algorithm design. [0003] In order to reduce the processing delay of cloud resource scheduling and improve the efficiency of cloud resource scheduling, evolutionary computing is currently used to schedule cloud resources, mainly using randomized data as input, and solving suboptimal results through a sufficient number of iterations . Since the acceptable suboptimal solution is used to approach the optimal solution, the time cost of problem solving is reduced, and it is suitable for ...

AI Technical Summary

Problems solved by technology

[0004] At present, the scheduling algorithms mainly used in cloud resource scheduling technology are divided into three categories: one is the integer programming algorithm, which can obtain the optimal mapping of cloud resources when the optimal solution exists, but its defect lies in the calculation and analysis delay. The second is supervised machine learning algorithms, which require a large amount of historical data as the support of the scheduling algorithm, and require a large number of iterations to approach the optimal solution. The obvious dynamic and uncertain characteristics of cloud resources challenge the accuracy and effectiveness of such algorithms; the third is evolutionary computing algorithms, which can obtain acceptable suboptimal solutions within a limited number of iterations, and can Meet the application requirements of cloud resource scheduling

[0005] Aiming at the problem of efficient mapping of virtual machine requirements to physical machine resources in the cloud environment, a scheduling algorithm with low latency and parallelism is needed as a support to solve the problem of high analysis latency in the scheduling process of traditional scheduling algorithms

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