Modeling Active Virtual Machines on IaaS Clouds Using an M/G/m/m plus K Queue
Modeling Active Virtual Machines on IaaS Clouds Using an M/G/m/m plus K Queue
复制标题
使用 M/G/m/m plus K 队列对 IaaS 云上的活动虚拟机进行建模
DOI：
10.1109/tsc.2014.2376563
复制
发表时间：
2016-05-01
影响因子：
8.1
通讯作者：
Liu, Jiqiang
中科院分区：
文献类型：
--
作者：
Chang, Xiaolin;Wang, Bin;Liu, Jiqiang
本文开发了一种新颖的近似分析模型，使用 M/G/m/m+K 队列评估 IaaS 云中活动虚拟机的性能。所提出的模型与基于变换的分析方法相结合，能够计算系统中作业数量的概率分布，以及随后的一组性能度量，包括系统中作业的平均数量、平均响应时间、立即服务的概率和阻塞概率。与现有的云数据中心马尔可夫模型相比，即使在中型 IaaS 云中服务时间分布具有较大变异系数（>1.5）时，我们的方法也可以更准确地反映系统行为。从所提出的分析模型获得的数值结果通过各种系统参数设置下的广泛模拟进行了验证，并与现有模型的结果进行了比较。
This paper develops a novel approximate analytical model to evaluate the performance of active virtual machines in IaaS clouds using an M/G/m/m+K queue. The proposed model, combined with the transform-based analytical approach, enables the computation of the probability distribution of the number of jobs in the system and subsequently a set of performance measures, including the mean number of jobs in the system, the mean response time, the probability of immediate service, and the blocking probability. Compared to the existing Markov models of cloud data centers, our approach can reflect the system behavior more accurately even when the service-time distribution has a large coefficient of variation (>1.5) in a medium-sized IaaS cloud. Numerical results obtained from the proposed analytical model are verified through extensive simulations under various system parameter settings, and compared with the results from existing models.