基于Markov过程的IaaS系统可用性建模与分析方法

doi:10.11772/j.issn.1001-9081.2019122245

计算机应用 ›› 2020, Vol. 40 ›› Issue (10): 3013-3018.DOI: 10.11772/j.issn.1001-9081.2019122245

基于Markov过程的IaaS系统可用性建模与分析方法

杨哂哂¹, 吴慧珍¹, 庄黎丽¹, 吕宏武²

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

收稿日期:2020-01-07 修回日期:2020-06-07 出版日期:2020-10-10 发布日期:2020-07-08
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:
国家自然科学基金资助项目（6187060169）。

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

YANG Shenshen¹, WU Huizhen¹, ZHUANG Lili¹, LYU Hongwu²

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).

摘要/Abstract

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

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)

中图分类号:

TP393

杨哂哂, 吴慧珍, 庄黎丽, 吕宏武. 基于Markov过程的IaaS系统可用性建模与分析方法[J]. 计算机应用, 2020, 40(10): 3013-3018.

YANG Shenshen, WU Huizhen, ZHUANG Lili, LYU Hongwu. Markov process-based availability modeling and analysis method of IaaS system[J]. Journal of Computer Applications, 2020, 40(10): 3013-3018.

参考文献

[1] 李阿妮, 张晓, 赵晓南, 等. 面向IaaS的云计算系统可用性评估[J]. 计算机科学,2016,43(10):33-39.(LI A N,ZHANG X, ZHAO X N,et al. Cloud computing system availability evaluation for IaaS[J]. Computer Science,2016,43(10):33-39.)
[2] URIARTE R B,DE NICOLA R,SCOCA V,et al. Defining and guaranteeing dynamic service levels in clouds[J]. Future Generation Computer Systems,2019,99:27-40.
[3] TORQUATO M,UMESH I M,MACIEL P. Models for availability and power consumption evaluation of a private cloud with VMM rejuvenation enabled by VM live migration[J]. The Journal of Supercomputing,2018,74(9):4817-4841.
[4] THAKKAR R,TRIVEDI R,BHAVSAR M. Experimenting with energy efficient VM migration in IaaS cloud:moving towards green cloud[C]//Proceedings of the 2017 International Conference on Future Internet Technologies and Trends,LNICST 220. Cham:Springer,2017:56-65.
[5] JIN H,LI Z,ZOU D,et al. DSEOM:a framework for dynamic security evaluation and optimization of MTD in container-based cloud[J]. IEEE Transactions on Dependable Secure Computing, 2019(Early Access):1-1.
[6] SEBASTIO S, GHOSH R, MUKHERJEE T. An availability analysis approach for deployment configurations of containers[J]. IEEE Transactions on Services Computing,2017(Early Access):1-1.
[7] KHAZAEI H,MIŠIĆ J,MIŠIĆ V B. Performance analysis of cloud centers under burst arrivals and total rejection policy[C]//Proceedings of the 2011 IEEE Global Telecommunications Conference. Piscataway:IEEE,2011:1-6.
[8] LV S,HILLSTON J,PIHO P,et al. An attribute-based availability model for large scale IaaS clouds with CARMA[J]. IEEE Transactions on Parallel and Distributed Systems,2020,31(3):733-748.
[9] CHANG X,XIA R,MUPPALA J K,et al. Effective modeling approach for IaaS data center performance analysis under heterogeneous workload[J]. IEEE Transactions on Cloud Computing,2018,6(4):991-1003.
[10] ATAIE N, ENTEZARI-MALEKI R, RASHIDI L, et al. Hierarchical stochastic models for performance,availability,and power consumption analysis of IaaS clouds[J]. IEEE Transactions on Cloud Computing,2019,7(4):1039-1056.
[11] CARVALHO M,MENASCÉ D A,BRASILEIRO F. Capacity planning for IaaS cloud providers offering multiple service classes[J]. Future Generation Computer Systems,2017,77:97-111.
[12] LIU B, CHANG X, HAN Z, et al. Model-based sensitivity analysis of IaaS cloud availability[J]. Future Generation Computer Systems,2018,83:1-13.
[13] LUO L,MENG S,QIU X,et al. Improving failure tolerance in large-scale cloud computing systems[J]. IEEE Transactions on Reliability,2019,68(2):620-632.
[14] GHOSH R,LONGO F,FRATTINI F,et al. Scalable analytics for IaaS cloud availability[J]. IEEE Transactions on Cloud Computing,2014,2(1):57-70.
[15] HUANG Y, KINTALA C, KOLETTIS N, et al. Software rejuvenation:analysis, module and applications[C]//Proceedings of the 25th International Symposium on Fault-Tolerant Computing. Piscataway:IEEE,1995:381-390.

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李卓, 宋子晖, 沈鑫, 陈昕. 边缘计算支持下的移动群智感知本地差分隐私保护机制[J]. 计算机应用, 2021, 41(9): 2678-2686.
[2]	陈家豪, 殷新春. 基于云雾计算的可追踪可撤销密文策略属性基加密方案[J]. 计算机应用, 2021, 41(6): 1611-1620.
[3]	葛丽娜, 胡雨谷, 张桂芬, 陈园园. 云计算环境基于客体属性匹配的逆向混合访问控制方案[J]. 计算机应用, 2021, 41(6): 1604-1610.
[4]	杨翎, 姜春茂. 基于三支决策的虚拟机节能迁移策略[J]. 计算机应用, 2021, 41(4): 990-998.
[5]	刘向宇, 夏国平, 夏秀峰, 宗传玉, 朱睿, 李佳佳. 个性化时空数据隐私保护[J]. 计算机应用, 2021, 41(3): 643-650.
[6]	吕佳玉, 竺智荣, 姚志强. 云计算环境下的双通道数据动态加密策略[J]. 计算机应用, 2020, 40(8): 2268-2273.
[7]	陈程军, 毛莺池, 王绎超. 基于激活-熵的分层迭代剪枝策略的CNN模型压缩[J]. 计算机应用, 2020, 40(5): 1260-1265.
[8]	郭曙杰, 李志华, 蔺凯青. 云环境下基于模糊隶属度的虚拟机放置算法[J]. 计算机应用, 2020, 40(5): 1374-1381.
[9]	许英鑫, 孙磊, 赵建成, 郭松辉. 基于蚁群优化算法的虚拟现场可编程门阵列部署策略[J]. 计算机应用, 2020, 40(3): 747-752.
[10]	王庆永, 毛莺池, 王绎超, 王龙宝. 基于多微云协作的计算任务卸载[J]. 计算机应用, 2020, 40(2): 328-334.
[11]	刘福鑫, 李劲巍, 王熠弘, 李琳. 基于Kubernetes的云原生海量数据存储系统设计与实现[J]. 计算机应用, 2020, 40(2): 547-552.
[12]	刘向宇, 陈金梅, 夏秀峰, Manish Singh, 宗传玉, 朱睿. 防止暴露位置攻击的轨迹隐私保护[J]. 计算机应用, 2020, 40(2): 479-485.
[13]	林立, 熊金波, 肖如良, 林铭炜, 陈秀华. Gaming@Edge:基于边缘节点的低延迟云游戏系统[J]. 计算机应用, 2019, 39(7): 2001-2007.
[14]	徐雅斌, 彭宏恩. 基于需求预测的PaaS平台资源分配方法[J]. 计算机应用, 2019, 39(6): 1583-1588.
[15]	李启锐, 彭志平, 崔得龙, 何杰光. 容器云环境虚拟资源配置策略的优化[J]. 计算机应用, 2019, 39(3): 784-789.