摘要
受日趋严格的环境标准的影响,高能耗和高污染成为制约云数据中心发展的重要因素。在保证云数据中心服务质量的前提下,应用动态电压调整技术,引入同步多重工作休眠模式,提出一种新型的虚拟机调度策略。通过构建具有自适应服务率和多重工作休假的二维连续时间马尔可夫(Markov)随机模型,运用矩阵几何解方法,从系统节能水平和用户请求平均延迟等方面评估虚拟机调度策略的性能。综合理论分析结果和仿真统计结果,验证虚拟机调度策略的有效性。从经济学角度出发,建立系统效用函数,改进萤火虫智能优化算法,给出工作休眠参数的优化方案,实现系统响应性能和节能效果之间的合理平衡。
Due to the increasingly strict environmental standards, high pollution and high energy consumption have be- come 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 establish- ing 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 cor-structed from the perspective of economics and design a researching algorithm of the sleep parameter based on the firefly algorithm （FA）.
出处
《通信学报》
EI
CSCD
北大核心
2017年第12期10-20,共11页
Journal on Communications
基金
国家自然科学基金资助项目(No.61472342)
河北省自然科学基金资助项目(No.F2017203141)~~
关键词
云数据中心
虚拟机调度策略
双速率
多重工作休假排队
效用函数
智能优化
cloud data centers, virtual machine scheduling strategy, dual-speed, multiple working vacations queue, utility function, intelligent optimization
