Cloud resource allocation method and system based on multi-objective optimization
A technology of multi-objective optimization and allocation method, applied in the direction of resource allocation, multi-program device, program control design, etc., can solve problems such as cloud resource allocation
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[0245] The experiment in this paper is run on the PlatEMO platform. The experimental environment is: Intel Core i5, 12GB RAM, Windows 10 (64bit), Matlab 2016b. The initial data used in the experiment is as follows: Fast non-dominated sorting optimization genetic algorithm based on sequential search to solve practical problems See Table 1 for the time parameter settings, Table 2 for the initial value of the virtual machine resources in the experiment, Table 3 for the initial value of the physical machine resources in the experiment, and Table 4 for the effectiveness of the virtual machine allocated to the physical machine in the experiment .
[0246] Table 1 parameter setting
[0247]
[0248] Table 2 The initial value of virtual machine resources in the experiment
[0249]
[0250] Table 3 Initial value of physical machine resources in the experiment
[0251]
[0252] Table 4 The effectiveness of virtual machines assigned to physical machines in the experiment
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