融合双速率和工作休眠的虚拟机调度策略及参数优化

doi:10.11959/j.issn.1000-436x.2017298

Abstract

Abstract:

Due to the increasingly strict environmental standards,high pollution and high energy consumption have become the significant factors restricting the development of cloud data centers (CDC).Under the premise of guaranteeing the quality of service (QoS) of CDC,dynamic power management (DPM) technology was applied,synchronous multiple working sleep mode was introduced,and a novel virtual machine (VM) scheduling strategy was proposed.By establishing a two-dimensional continuous-time Markov stochastic model with adaptive service rate and synchronous multiple work vacations,and using the method of a matrix geometric solution,the performance of the VM scheduling strategy was evaluated in terms of energy saving level and average delay of requests.Numerical results with analysis and simulation verify the energy saving effectiveness of the VM scheduling strategy.In order to achieve a reasonable balance between the response performance and the energy-saving effect,a system utility function was constructed from the perspective of economics and design a researching algorithm of the sleep parameter based on the firefly algorithm(FA).

Key words: cloud data centers, virtual machine scheduling strategy, dual-speed, multiple working vacations queue, utility function, intelligent optimization

CLC Number:

TP393

Shun-fu JIN,Shan-shan HAO,Bao-shuai WANG. Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization[J]. Journal on Communications, 2017, 38(12): 10-20.

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References 16

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系统水平	初始的虚拟机状态	一步转移后的虚拟机状态
[0,θ₂-1]	状态Ⅰ	状态Ⅰ
θ₂	状态Ⅰ	状态Ⅰ
θ₂	状态Ⅱ	状态Ⅰ或状态Ⅱ
[θ₂+1，θ₁-1]	状态Ⅰ	状态Ⅰ
[θ₂+1，θ₁-1]	状态Ⅱ	状态Ⅱ
θ₁	状态Ⅰ	状态Ⅰ或状态Ⅱ
θ₁	状态Ⅱ	状态Ⅱ
[θ₁+1,n]	状态Ⅱ	状态Ⅱ
n+1	状态Ⅱ	状态Ⅱ
n+1	状态Ⅲ	状态Ⅱ或状态Ⅲ
[n+2,n+m+θ₃]	状态Ⅱ	状态Ⅱ
[n+2,n+m+θ₃]	状态Ⅲ	状态Ⅲ
(n+m+θ₃+1,∞）	状态Ⅱ	状态Ⅱ或状态Ⅲ
(n+m+θ₃+1,∞）	状态Ⅲ	状态Ⅲ

参数	值
主模块中虚拟机的数量n	30
备用模块中虚拟机的数量m	20
双阈值θ₁和θ₂	20,10
用户请求的到达率λ	15
低速运行服务率μ_l	0.2
高速运行服务率μ_h	0.4
能耗因子k	10

μ_s	θ₃	δ^*	F(δ^*)
	20	0.022 7	41.988 3
0.10	30	0.027 2	42.356 8
0.10	40	0.033 3	42.671 5
	50	0.042 2	42.931 8
	20	0.019 5	42.248 3
0.11	30	0.023 4	42.616 4
0.11	40	0.028 9	42.934 2
	50	0.037 0	43.198 8
	20	0.016 1	42.547 4
0.12	30	0.019 4	42.908 2
	40	0.024 1	43.224 6
	50	0.031 2	43.491 4

Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization

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