服务水平协议感知的云中心利润最大化资源配置

doi:10.11772/j.issn.1001-9081.2015.06.1585

计算机应用 ›› 2015, Vol. 35 ›› Issue (6): 1585-1589.DOI: 10.11772/j.issn.1001-9081.2015.06.1585

服务水平协议感知的云中心利润最大化资源配置

何怀文^1,2, 傅瑜¹, 杨亮¹

1. 电子科技大学中山学院计算机学院, 广东中山 528402;
2. 中山大学信息科学与技术学院, 广州 510275

收稿日期:2015-01-08 修回日期:2015-04-02 发布日期:2015-06-12
通讯作者: 何怀文(1980-),男,广西北海人,讲师,博士研究生,主要研究方向:云计算、资源分配、随机优化;hehuaiwen@gmail.com
作者简介:傅瑜(1962-),男,陕西西安人,教授,博士,主要研究方向:模式识别、网络安全;杨亮(1980-),男,江西婺源人,讲师,博士研究生,主要研究方向:智能控制、优化算法。
基金资助:
国家自然科学基金资助项目(61300095);广东省自然科学基金资助项目 (S2012040011123);广东省自然科学基金资助项目(S2012010010508);中山市科技计划项目(2013A3FC0285,2014A2FC396)。

Service layer agreement-aware resource allocation for cloud center profit maximization

HE Huaiwen^1,2, FU Yu¹, YANG Liang¹

1. School of Computer Engineering, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan Guangdong 528402, China;
2. School of Information Science and Technology, Sun Yat-Sen University, Guangzhou Guangdong 510275, China

Received:2015-01-08 Revised:2015-04-02 Published:2015-06-12

摘要/Abstract

摘要：

针对云计算中心优化资源配置以实现利润最大化的问题,提出基于服务水平协议(SLA)感知的云中心服务器数量和运行速率优化的分析模型,同时考虑了云中心能耗成本、服务器租用成本、用户等待时间、SLA违规惩罚等重要因素。通过数值仿真分析了服务器数量、运行速率变化对云中心利润的影响。数值仿真结果表明:在请求速率固定时,通过优化服务器数量、运行速率可使云中心获得最大利润;而随着请求速率的增加,优化服务器数量和运行速率将会使云中心利润呈线性增长。分析结果可为云服务提供商获得更好的收益提供参考方法。

关键词: 云中心, 利润最大化, 资源配置, 服务水平协议感知, 等待时间

Abstract:

For the problem of optimizing resource allocation to achieve profit maximization of cloud computing center, an analysis model based on Service Layer Agreement (SLA)-aware was proposed for optimizing server number and speed of cloud center. Meanwhile some important factors were taken into account, such as energy cost, server rental cost, customer waiting time, and SLA violation penalty. The impacts of cloud center profit by changing server number and speed were analyzed by numerical simulation. The numerical simulation results indicate that cloud center will obtain maximum profit by optimizing server number and speed at a certain request rate; with request rate increasing, profit will increase linearly by optimizing server number and speed. The analysis results can provide a reference method for cloud service provider to improve net business gain.

Key words: cloud center, profit maximization, resource allocation, Service Layer Agreement (SLA)-aware, waiting time

中图分类号:

TP393.02

何怀文, 傅瑜, 杨亮. 服务水平协议感知的云中心利润最大化资源配置[J]. 计算机应用, 2015, 35(6): 1585-1589.

HE Huaiwen, FU Yu, YANG Liang. Service layer agreement-aware resource allocation for cloud center profit maximization[J]. Journal of Computer Applications, 2015, 35(6): 1585-1589.

参考文献

[1] BUYYA R, YEO C S, VENUGOPAL S, et al. Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility [J]. Future Generation Computer Systems, 2009, 25(6): 599-616.
[2] ARMBRUST M, FOX A, GRIFFITH R, et al. A view of cloud computing [J]. Communications of the ACM, 2010, 53(4): 50-58.
[3] LEE Y C, WANG C, ZOMAYA A Y, et al. Profit-driven service request scheduling in clouds [C]//Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. Piscataway: IEEE, 2010: 15-24.
[4] CAO J, HWANG K, LI K, et al. Optimal multiserver configuration for profit maximization in cloud computing [J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(6): 1087-1096.
[5] KHAZAEI H, MISIC J, MISIC V B. Performance analysis of cloud computing centers using M/G/m/m+r queuing systems [J]. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(5): 936-943.
[6] BELOGLAZOV A. Energy-efficient management of virtual machines in data centers for cloud computing [D]. Melbourne: University of Melbourne, 2013.
[7] GARG S K, GOPALAIYENGAR S K, BUYYA R. SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter [C]//Proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing, LNCS 7016. Berlin:Springer-Verlag, 2011: 371-384.
[8] XIONG K, PERROS H. SLA-based resource allocation in cluster computing systems [C]//Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing. Piscataway: IEEE, 2008: 1-12.
[9] VAN H N, TRAN F D, MENAUD J M. SLA-aware virtual resource management for cloud infrastructures [C]//Proceedings of the 9th IEEE International Conference on Computer and Information Technology. Piscataway: IEEE, 2009, 1: 357-362.
[10] CHUN B N, CULLER D E. User-centric performance analysis of market-based cluster batch schedulers [C]//Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid. Piscataway: IEEE, 2002: 30-39.
[11] IRWIN D E, GRIT L E, CHASE J S. Balancing risk and reward in a market-based task service [C]//Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing. Piscataway: IEEE, 2004: 160-169.
[12] XIONG K. Power-aware resource provisioning in cluster computing[C]//Proceedings of the 2012 IEEE International Symposium on Parallel & Distributed Processing. Piscataway: IEEE, 2010: 1-11.
[13] MAZZUCCO M, DYACHUK D. Balancing electricity bill and performance in server farms with setup costs [J]. Future Generation Computer Systems, 2012, 28(2): 415-426.
[14] LU C .Queuing theory [M]. Beijing: Beijing University of Posts and Telecommunications Press, 1994: 100-110.(陆传赉.排队论[M].北京: 北京邮电学院出版社, 1994:100-110.)
[15] TANG Y, TANG X. Queuing theory — basic and analysis [M]. Beijing: Science Press, 2006: 29-40.(唐应辉, 唐小我, 排队论——基础与分析技术 [M].北京: 科学出版社, 2006:29-40.)
[16] MCGOVERN G. Selfish, mean, impatient customers [EB/OL]. [2014-12-05]. http://www.cmswire.com/cms/web-content/selfish-mean-impatient-customers-002891.php.
[17] ZHURAVLEV S, SAEZ J C, BLAGODUROV S, et al. Survey of energy-cognizant scheduling techniques [J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(7): 1447-1464.

服务水平协议感知的云中心利润最大化资源配置

Service layer agreement-aware resource allocation for cloud center profit maximization

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

[1]	杨志军, 刘征, 丁洪伟. 连续时间完全服务与门限服务两级轮询系统性能研究[J]. 计算机应用, 2019, 39(7): 2019-2023.
[2]	张兴盛, 余敦辉, 张万山, 王晨旭. 时空众包环境下时效均衡的在线任务分配算法[J]. 计算机应用, 2019, 39(5): 1357-1363.
[3]	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.
[4]	黄业文, 邝神芬, 杨荣领, 杨春侠. 非强占有限优先权M/G/1排队系统[J]. 计算机应用, 2016, 36(7): 1779-1783.
[5]	郑红星, 吴岳, 涂闯, 刘进平. 单船岸桥分配与调度集成优化模型[J]. 计算机应用, 2015, 35(1): 247-251.
[6]	何怀文, 傅瑜, 杨亮, 杨毅红. 性能受限下云中心异构服务器的能耗优化[J]. 计算机应用, 2015, 35(1): 39-42.
[7]	杨镜吴磊武德安王晓敏刘念伯. 云平台下动态任务调度人工免疫算法[J]. 计算机应用, 2014, 34(2): 351-356.
[8]	邬平吴斌李鑫李俊黄红伟. 利用多维统计方法预测网络拥塞状态[J]. 计算机应用, 2012, 32(05): 1251-1254.
[9]	齐金平查显锋. 多任务多资源优化调度的病毒遗传算法[J]. 计算机应用, 2011, 31(07): 1773-1775.
[10]	唐朝伟邵艳清陈宏旦刘鹏军郭春旺. 提高移动站点综合节能性能的新算法及性能分析[J]. 计算机应用, 2009, 29(09): 2420-2423.