具备适应性调节服务机制的云资源优化分析  

Cloud Resource Optimization Analysis with Adaptive Adjustment Service Mechanism

作　　者：张永华 徐秀丽[1] ZHANG Yonghua;XU Xiuli(School of Science,Yanshan University,Qinhuangdao Hebei 066004,China)

机构地区：[1]燕山大学理学院,河北秦皇岛066004

出　　处：《重庆师范大学学报（自然科学版）》2023年第6期15-24,共10页Journal of Chongqing Normal University:Natural Science

基　　金：国家自然科学基金面上项目(No.62171143)。

摘　　要：为了降低云数据中心能量浪费和提高云用户服务质量,并向云计算平台管理者提供合理的顾客收费策略,提出了虚拟机服务模式适应性调节的调度策略。以当前系统请求节点数为依据,动态调整系统服务速率及模式,并对虚拟机进行分区管理。基于工作故障的部分服务台同步休假M/M/c+k/d排队系统对该服务平台进行建模分析,利用迭代求解的方法求得了系统的稳态分布及性能指标。通过数值分析展示了各参数对性能指标的影响,结果表明:在Ⅱ区增加12台虚拟机时,发生溢出故障的概率比Ⅰ区低7.3%,且该调度策略表现出比较明显的节能效果。此外,通过构造收益函数,求解得到同时满足个人收益均衡和社会最优收益的顾客收费策略。该模型的建立将为云计算平台的优化管理和控制提供参考和依据。In order to reduce the energy waste of cloud data center and improve the Quality of Service(QoS),a adaptive scheduling policy of virtual machine(VM) service mode is proposed.Based on this policy,a reasonable customer charge scheme is developed for the cloud computing platform manager.According to the current number of system request nodes,the service rate and service mode of the system are dynamically adjusted in order that VMs can be managed by partitions.The service platform is modeled based on the M/M/c+k/d queueing system with partial servers synchronous vacation and working breakdowns,then the stationary distribution and performance indices of the system is obtained by using the iterative method.The numerical analysis demonstrates the influence of system parameter on the performance indices.The results show that the overflow probability of adding 12 VMs in zone II is 7.3% lower than that of adding 12 VMs in zone I.Meanwhile,the scheduling policy has a significant energy conservation effect.Furthermore,by constructing the benefit function,the customer charging strategy that modulates individual profit and social profit is derived.The establishment of this model will provide theory basis and tool for the optimization management and control of the cloud computing platform.

关 键 词：排队系统 云计算 调度策略 节能 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]