基于Markov过程的IaaS系统可用性建模与分析方法  

Markov process-based availability modeling and analysis method of IaaS system

作　　者：杨哂哂[1] 吴慧珍 庄黎丽 吕宏武[2] YANG Shenshen;WU Huizhen;ZHUANG Lili;LYU Hongwu(Network Center,Shanghai Business University,Shanghai 200235,China;College of Computer Science and Technology,Harbin Engineering University,Harbin Heilongjiang 150001,China)

机构地区：[1]上海商学院网络中心,上海200235 [2]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001

出　　处：《计算机应用》2020年第10期3013-3018,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(6187060169)。

摘　　要：针对现有基础设施即服务(IaaS)可用性模型难以计算存在多个可用物理机器(PM)概率的问题,提出一种基于Markov过程的IaaS可用性分析方法。首先,将计算资源划分为hot PM、warm PM和cold PM三类;然后,结合资源分配过程的相应阶段对可用性影响进行建模,分别生成对应的三种分配子模型,子模型之间通过不同种类计算资源的转换关系相互协作,构建系统整体模型;其次,基于Markov过程建立方程组以对可用性模型进行求解;最后,结合实例对分析模型进行验证,并对PM变迁速率等关键影响因素进行了分析。实验结果表明,增加PM尤其是cold PM的数量有助于提升IaaS的可用性。所提方法可以用于评估IaaS存在一个或多个可用PM的概率。Concerning the problem that existing availability models of Infrastructure as a Service(IaaS)are difficult to calculate the probability of the existence of multiple available Physical Machines(PMs),a new availability analysis method based on Markov process was proposed for IaaS clouds.Firstly,the computing resources were divided into three types:hot PM,warm PM and cold PM.Then,the impact of availability was modeled by combining the corresponding stages of the resource allocation process,separately generating three kinds of allocation sub-models.These sub-models cooperated with each other through the transformation relationships of different types of computing resources,so as to construct the overall model of the system.After that,the availability model was solved by equations constructed based on Markov process.Finally,the proposed analysis model was verified with a practical example,and the key factors such as PM transition rate were analyzed.Experimental results show that,increasing the number of PMs,especially cold PMs helps to improve the availability of IaaS.The proposed method can be used to estimate the probability of the existence of one or multiple available PMs.

关 键 词：云计算 可用性 MARKOV过程 稳态概率 物理机器 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]