基于清华云监控平台的云迁移性能

doi:10.11772/j.issn.1001-9081.2015.11.3026

计算机应用 ›› 2015, Vol. 35 ›› Issue (11): 3026-3030.DOI: 10.11772/j.issn.1001-9081.2015.11.3026

• 2015年全国开放式分布与并行计算学术年会(DPCS 2015)论文 • 上一篇下一篇

基于清华云监控平台的云迁移性能

马海峰^1,2, 意合巴力^2,3, 王烨⁴, 杨家海², 张潮⁴

1. 黑龙江科技大学计算机与信息工程学院, 哈尔滨 150022;
2. 清华大学网络科学与网络空间研究院, 北京 100084;
3. 新疆广电网络股份有限公司, 乌鲁木齐 830091;
4. 中国移动通信集团公司, 北京 100032

收稿日期:2015-06-17 修回日期:2015-07-21 出版日期:2015-11-10 发布日期:2015-11-13
通讯作者: 马海峰(1977-),男,黑龙江哈尔滨人,副教授,博士,CCF会员,主要研究方向:云监控、云安全.
作者简介:意合巴力(1977-),女(维吾尔族),新疆乌鲁木齐人,工程师,硕士,主要研究方向:计算机网络; 王烨(1975-),男,陕西西安人,高级工程师,博士,主要研究方向:移动通信、大数据分析、网络管理; 杨家海(1966-),男,浙江云和人,教授,博士,CCF会员,主要研究方向: 计算机网络; 张潮(1980-),男,吉林榆树人,高级工程师,硕士,主要研究方向:移动通信、网络管理.
基金资助:
教育部-中国移动研究基金资助项目(MCM20123041);黑龙江省教育厅科学技术研究项目(12533052).

Cloud migration performance of tsinghua cloud monitoring platform

MA Haifeng^1,2, YI Hebali^2,3, WANG Ye⁴, YANG Jiahai², ZHANG Chao⁴

1. Computer and Information Engineering Institute, Heilongjiang University of Science and Technology, Harbin Helongjiang 150022, China;
2. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084, China;
3. Xinjiang Broadcast and Television Company Limited, Urumqi Xinjiang 830091, China;
4. China Mobile Communications Corporation, Beijing 100032, China

Received:2015-06-17 Revised:2015-07-21 Online:2015-11-10 Published:2015-11-13

摘要/Abstract

摘要： 当云计算技术兴起后,很多企业已经或计划将业务和应用迁移到云上,而这面临着应用性能可能降低,关键业务和应用安全性受到威胁等问题,因此是否云迁移还是部署到独立服务器上,是一个需要深入研究的问题.以清华云平台为基础,搭建了基于Nagios的清华云监控平台,先对清华云平台和架构进行介绍,再对Nagios和清华云监控平台架构进行了论述,在云迁移性能评估中,以Ubuntu和Windows为操作系统平台,以CPU负载和内存使用为评估指标,分别在云服务器和独立服务器上运行CPU运算类和服务器负载类两类应用,最后对实验结果进行了分析和比较.实验表明,部分应用在独立服务器上性能更好,并不适合云迁移.

关键词: 云计算, 性能监控, 云迁移, OpenStack, Nagios

Abstract: With the popularization of cloud computing technology, many enterprises have migrated or are planning to migrate their business and applications to the cloud. But it may face the problems of application performance degradation, and the key business and applications may suffer security threats. Therefore, migrating to cloud or deploying in independent server is a problem that needs to be further studied. In this paper, based on the Tsinghua cloud platform, Tsinghua cloud monitoring platform was set up based on Nagios. Firstly, Tsinghua cloud platform and its architecture were introduced, and then Nagios and the architecture of Tsinghua cloud monitoring platform were discussed. For cloud migration performance evaluation, Ubuntu and Windows were used as operating system platforms, CPU load and memory usage were used as evaluation indexes, two applications of CPU computing type and server load type respectively ran on the cloud server and the independent server. At last, the experimental results were analyzed and compared. The experimental result shows that some applications have better performance on independent servers that may not be suitable for migrating to cloud platform.

Key words: cloud computing, performance monitoring, cloud migration, OpenStack, Nagios

中图分类号:

TP393
TP311

马海峰, 意合巴力, 王烨, 杨家海, 张潮. 基于清华云监控平台的云迁移性能[J]. 计算机应用, 2015, 35(11): 3026-3030.

MA Haifeng, YI Hebali, WANG Ye, YANG Jiahai, ZHANG Chao. Cloud migration performance of tsinghua cloud monitoring platform[J]. Journal of Computer Applications, 2015, 35(11): 3026-3030.

参考文献

[1] ACETO G, BOTTA A, DONATO W D, et al. Cloud monitoring: a survey[J]. Computer Networks, 2013, 57(9): 2093-2115.
[2] FATEMA K, EMEAKAROHA V C, HEALY P D, et al. A survey of cloud monitoring tools: taxonomy, capabilities and objectives[J]. Journal of Parallel and Distributed Computing, 2014, 74(10): 2918-2933.
[3] KHAJEH-HOSSEINI A, GREENWOOD D, SOMMERVILLE I. Cloud migration: a case study of migrating an enterprise it system to IaaS[C]// Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing. Piscataway: IEEE, 2010: 450-457.
[4] LIU Q, LIU L, BI X. Discussion of cloud migration for service platform[J]. Telecom Engineering Technics and Standardization, 2012, 25(4): 35-38.(刘芹, 刘玲, 毕晓飞. 业务平台云迁移方案的探讨[J]. 电信工程技术与标准化,2012, 25(4): 35-38.)
[5] SHEN Q, DONG B, XIAO D. Design and implementation of cloud monitoring system based on server clusters[J]. Computer Engineering and Science, 2012,34(10):73-77.(沈青,董波,肖德宝. 基于服务器集群的云监控系统设计与实现[J]. 计算机工程与科学,2012, 34(10): 73-77.)
[6] Nagios[EB/OL].[2015-01-22].http://www.nagios.org.
[7] ZHANG Z. The application research of resource monitoring under cloud computing environment[D]. Beijing: Northern China University of Technology, 2013: 16-22.(张仲妹. 云计算环境下的资源监控应用研究[D].北京:北方工业大学,2013: 16-22.)
[8] KUMAR R, GUPTA N, CHARU S, et al. Open source solution for cloud computing platform using OpenStack[J]. International Journal of Computer Science and Mobile Computing, 2014, 3(5): 89-98.
[9] WEN X, GU G, LI Q, et al. Comparison of open-source cloud management platforms: OpenStack and OpenNebula[C]// Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery. Piscataway: IEEE, 2012:2457-2461.
[10] CHEN B, CHENG Z. Cloud computing and OpenStack[M].Beijing: Publishing House of Electronics Industry Press, 2013:4-25.(陈伯龙,程志鹏. 云计算与OpenStack[M]. 北京: 电子工业出版社,2013:4-25.)
[11] WANG F. The construction and realization monitoring platform of application servers based on Nagios[J]. Research and Exploration in Laboratory, 2010,29(12):51-57.(王帆. 基于Nagios的服务器监控平台构建与实现[J]. 实验室研究和探索,2010,29(12):51-57.)

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基于清华云监控平台的云迁移性能

Cloud migration performance of tsinghua cloud monitoring platform

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