Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 3013-3018.DOI: 10.11772/j.issn.1001-9081.2019122245

• Computer software technology • Previous Articles     Next Articles

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

YANG Shenshen1, WU Huizhen1, ZHUANG Lili1, LYU Hongwu2   

  1. 1. Network Center, Shanghai Business University, Shanghai 200235, China;
    2. College of Computer Science and Technology, Harbin Engineering University, Harbin Heilongjiang 150001, China
  • Received:2020-01-07 Revised:2020-06-07 Online:2020-10-10 Published:2020-07-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (6187060169).


杨哂哂1, 吴慧珍1, 庄黎丽1, 吕宏武2   

  1. 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
  • 通讯作者: 吕宏武
  • 作者简介:杨哂哂(1984-),女,河南信阳人,工程师,硕士,主要研究方向:网络安全、随机数据处理;吴慧珍(1980-),女,安徽泾县人,讲师,硕士,主要研究方向:商务英语教学、应用翻译;庄黎丽(1982-),女,上海金山人,讲师,硕士,主要研究方向:高校档案管理、信息安全;吕宏武(1983-),男,山东日照人,副教授,博士,CCF会员,主要研究方向:云计算可用性、网络安全。
  • 基金资助:

Abstract: 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.

Key words: cloud computing, availability, Markov process, steady state probability, Physical Machine (PM)

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。

关键词: 云计算, 可用性, Markov过程, 稳态概率, 物理机器

CLC Number: