SaaS-oriented modeling and analysis of load balancing strategy

doi:10.11772/j.issn.1001-9081.2017.01.0024

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (1): 24-30.DOI: 10.11772/j.issn.1001-9081.2017.01.0024

Previous Articles Next Articles

SaaS-oriented modeling and analysis of load balancing strategy

MING Li¹, LI Tong^1,2, QIN Jianglong^1,2, ZHENG Ming¹, JIANG Xudong¹, XIE Zhongwen^1,2

1. School of Software, Yunnan University, Kunming Yunnan 650500, China;
2. Key Laboratory in Software Engineering of Yunnan Province(Yunnan University), Kunming Yunnan 650500, China

Received:2016-07-18 Revised:2016-08-14 Online:2017-01-10 Published:2017-01-09
Supported by:
This work is partially supported by the Natural Science Foundation of China (61379032, 61262024, 61462092), the Science and Technology Program of Scientific Research Foundation in Education Department of Yunnan Province (2014Y12).

面向软件即服务的负载均衡策略建模与分析

明利¹, 李彤^1,2, 秦江龙^1,2, 郑明¹, 蒋旭东¹, 谢仲文^1,2

1. 云南大学软件学院, 昆明 650500;
2. 云南省软件工程重点实验室(云南大学), 昆明 650500

通讯作者: 秦江龙
作者简介:明利(1989-),女,河南安阳人,硕士研究生,主要研究方向:软件动态演化、云计算;李彤(1963-),男,河北石家庄人,教授,博士生导师,博士,CCF会员,主要研究方向:软件工程、软件过程;秦江龙(1984-),男,云南安宁人,讲师,博士,CCF会员,主要研究方向:软件过程、软件演化;郑明(1992-),男,安徽安庆人,硕士研究生,主要研究方向:软件演化、云计算;蒋旭东(1986-),男,湖南邵阳人,硕士研究生,主要研究方向:软件演化、软件过程;谢仲文(1982-),男,福建漳州人,讲师,博士,CCF会员,主要研究方向:软件动态演化、云计算、软件过程。
基金资助:
国家自然科学基金资助项目（61379032，61262024，61462092）；云南省教育厅科学研究基金理（工）科资助项目（2014Y012）。

Abstract

Abstract: To improve the efficiency of resource access in Software as a Service (SaaS) applications, a load balancing strategy combined with the important features of SaaS service was proposed. Firstly, the load balancing strategy was proposed by combining multi-tenancy and high scalability in SaaS service based on the distribution of request and global and local scalability. Secondly, the load balancing strategy model was constructed and simulated by a Petri net. Finally, this strategy was compared with Round Robin (RR), stochastic algorithm and Improved Least-Connection Scheduling (ILCS) load balancing algorithm in response time and throughput. The experimental results show that the response time and throughput of the proposed strategy become stable and they are superior to the other three algorithms after the request rate reaches 500 per second.

Key words: Software as a Service (SaaS), multi-tenancy, scalability, load balancing, Petri net

摘要： 为提高软件即服务（SaaS）应用中资源的访问效率，提出支持SaaS服务重要特征的负载均衡策略。首先，结合SaaS服务的多租户和高度可伸缩两大特性，提出一种基于租户请求分流、在局部和全局两个层次伸缩的负载均衡策略；其次，对所提出负载均衡策略用Petri网进行建模并仿真；最后，将提出的负载均衡策略与轮询（RR）、随机和改进的最小连接（ILCS）负载均衡算法在总体响应时间和总吞吐量两方面进行比较。实验结果表明：在请求速率达到500请求/秒后，所提策略的总体响应时间和总吞吐量趋于稳定并优于另外三种算法。

关键词: 软件即服务, 多租户, 可伸缩性, 负载均衡, Petri网

CLC Number:

TP311.5

MING Li, LI Tong, QIN Jianglong, ZHENG Ming, JIANG Xudong, XIE Zhongwen. SaaS-oriented modeling and analysis of load balancing strategy[J]. Journal of Computer Applications, 2017, 37(1): 24-30.

明利, 李彤, 秦江龙, 郑明, 蒋旭东, 谢仲文. 面向软件即服务的负载均衡策略建模与分析[J]. 计算机应用, 2017, 37(1): 24-30.

References

[1] 白海石.Windows Azure实战[M].北京:机械工业出版社,2014:4-80请补充所在文献的页码范围。.(BAI H S. Windows Azure in Action[M]. Beijing:China Machine Press, 2014:4-80.)
[2] GUO C J, SUN W, HUANG Y, et al. A framework for native multi-tenancy application development and management[C]//CEC 2007:Proceedings of the 9th IEEE International Conference on E-Commerce Technology. Washington, DC:IEEE Computer Society, 2007:551-558.
[3] PATHAK K K, YADAV P S, TIWARI R, et al. A modified approach for load balancing in cloud computing using extended honey bee algorithm[J]. International Journal of Research Review in Engineering Science and Technology, 2012, 3(1):12-19.
[4] WU L, GARG S K, BUYYA R. SLA-based resource allocation for Software as a Service provider (SaaS) in cloud computing environments[C]//Proceedings of the 201111th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. Washington, DC:IEEE Computer Society, 2011:195-204.
[5] 汪德帅,张一川,张斌,等.面向多租约SaaS应用的负载均衡机制研究与实现[J].小型微型计算机系统,2012,33(1):71-77.(WANG D S, ZHANG Y C, ZHANG B, et al. Research and implementation of load balancing mechanism for multi-tenant SaaS application[J]. Journal of Chinese Computer Systems, 2012, 33(1):71-77.)
[6] AVERSA L, BESTAVROS A. Load balancing a cluster of Web servers:using distributed packet rewriting[C]//IPCCC' 00:Proceedings of the 2000 IEEE International Conference on Performance, Computing, and Communications. Piscataway, NJ:IEEE, 2000:24-29.
[7] 陈燕升,张赞波,任江涛.改进的最小链接负载均衡调度算法[J].计算机系统应用,2015,24(7):88-92.(CHEN Y S, ZHANG Z B, REN J T. Improved minimum link load balancing scheduling algorithm[J]. Computer Systems & Applications, 2015, 24(7):88-92.)
[8] 郭成城,晏蒲柳.一种异构Web服务器集群动态负载均衡算法[J].计算机学报,2005,28(2):179-184.(GUO C C, YAN P L. A dynamic load-balancing algorithm for heterogeneous Web server cluster[J]. Chinese Journal of Computers, 2005, 28(2):179-184.)
[9] 赵少卡,李立耀,徐聪,等.基于SaaS的弹性云平台优化调度策略设计[J].计算机应用研究,2014,31(2):422-425.(ZHAO S K, LI L Y, XU C, et al. Elastic cloud platform optimal scheduling strategy design based on SaaS[J]. Application Research of Computers, 2014, 31(2):422-425.)
[10] DOMANAL S G, REDDY G R M. Load balancing in cloud computing using modified throttled algorithm[C]//Proceedings of the 2013 IEEE International Conference on Cloud Computing in Emerging Markets. Piscataway, NJ:IEEE, 2013:1-5.
[11] RATHORE R, SHARMA V, GOLA K K. A new approach for load balancing in cloud computing[J]. International Journal of Engineering and Computer Science, 2014, 2(5):1636-1640.
[12] MEHMOOD M, SATTAR K, KHAN A H, et al. Load balancing approach in cloud computing[J]. Journal of Information Technology & Software Engineering, 2015, 5(3):100153.
[13] KOKILAVANI T, AMALARETHINAM D I G. Load balanced min-min algorithm for static meta-task scheduling in grid computing[J]. International Journal of Computer Applications, 2011, 20(2):43-49.
[14] 熊伟,李兵,陈军,等.一种基于预测控制的SaaS系统自适应方法[J].计算机学报,2016,39(2):364-376.(XIONG W, LI B, CHEN J, et al. A self-adaption approach based on predictive control for SaaS[J]. Chinese Journal of Computers, 2016, 39(2):364-376.)
[15] 牟权,叶保留,陆桑璐.基于云计算的普适服务集成平台技术研究[EB/OL].[2016-03-15] . http://www.itfront.cn/attachment.aspx?attachmentid=3072.(MOU Q, YE B L, LU S L. A technology research of pervasive service integration platform based on cloud computing[EB/OL].[2016-03-15] . http://www.itfront.cn/attachment.aspx?attachmentid=3072.)
[16] 叶伟.互联网时代的软件革命:SaaS架构设计[M].北京:电子工业出版社,2009:32-47.(YE W. The Software Evolution of the Internet Age:SaaS Architecture Design[M]. Beijing:Publishing House of Electronics Industry, 2009:32-47.)
[16] 另外说明参考文献
[16] :撰写时出现失误:原参考文献为:Iod. Software as a service[M]. London:Director Publications Ltd, 2002. 现将参考文献
[16] 修改为:叶伟.互联网时代的软件革命:SaaS架构设计[M].北京:电子工业出版社,2009:32-47. (YE W. The Software Evolution of the Internet Age:SaaS Architecture Design[M]. Beijing:Publishing House of Electronics Industry, 2009:32-47.)Iod. Software as a Service[M]. London:Director Publications Ltd, 2002:补充所在文献的页码范围。.
[17] TSAI W T, BAI X Y, HUANG Y. Software-as-a-Service (SaaS):perspectives and challenges[J]. Science China Information Sciences, 2014, 57(5):1-15.
[18] 杜垚,郭涛,陈俊杰.云环境下机群弹性负载均衡机制[J].计算机应用,2013,33(3):830-833.(DU Y, GUO T, CHEN J J. Fleet elastic load balancing mechanism in cloud environment[J]. Journal of Computer Applications, 2013, 33(3):830-833.)
[19] 吴哲辉.Petri网导论[M].北京:机械工业出版社,2006:1-65.(WU Z H. Introduction of Petri Net[M]. Beijing:China Machine Press, 2006:1-65.)
[20] 林闯.随机PETRI网和系统性能评介[M].2版.北京:清华大学出版社,2005:19-62.(LIN C. Stochastic Petri Net and Performance Evaluation of Systems[M]. 2nd ed. Beijing:Tsinghua University Press, 2005:19-62.)
[21] 李彤.软件并行开发过程[M]. 北京:科学出版社,2003:11-15.(LI T. Concurrent Development Processes of Software[M]. Beijing:Science Press, 2003:11-15.)
[22] SINGH A, GOYAL P, BATRA S. An optimized round robin scheduling algorithm for CPU scheduling[J]. International Journal on Computer Science & Engineering, 2010, 2(7):2383-2385.

SaaS-oriented modeling and analysis of load balancing strategy

面向软件即服务的负载均衡策略建模与分析

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	ZHAO Quan, TANG Xiaochun, ZHU Ziyu, MAO Anqi, LI Zhanhuai. Low-latency cluster scheduling framework for large-scale short-time tasks [J]. Journal of Computer Applications, 2021, 41(8): 2396-2405.
[2]	QING Xinyi, CHEN Yuling, ZHOU Zhengqiang, TU Yuanchao, LI Tao. Blockchain storage expansion model based on Chinese remainder theorem [J]. Journal of Computer Applications, 2021, 41(7): 1977-1982.
[3]	ZHANG Lufei, SUN Rujun, QIN Fang. Message aggregation technology of runtime system for graph computing [J]. Journal of Computer Applications, 2021, 41(4): 984-989.
[4]	YANG Ling, JIANG Chunmao. Strategy of energy-aware virtual machine migration based on three-way decision [J]. Journal of Computer Applications, 2021, 41(4): 990-998.
[5]	XU Hongliang, YANG Guiqin, JIANG Zhanjun. Data center adaptive multi-path load balancing algorithm based on software defined network [J]. Journal of Computer Applications, 2021, 41(4): 1160-1164.
[6]	SHI Jinshan, LI Ru, SONG Tingting. Blockchain-based access control framework for Internet of things [J]. Journal of Computer Applications, 2020, 40(4): 931-941.
[7]	LI Cui, CHEN Qingkui. Dynamic monitoring model based on DPDK parallel communication [J]. Journal of Computer Applications, 2020, 40(2): 335-341.
[8]	LIU Weirong, ZHEN Hong. Modeling and simulation of container transportation process in blockchain based container sharing mode [J]. Journal of Computer Applications, 2019, 39(7): 2141-2147.
[9]	SHI Zhicai, WANG Yihan, ZHANG Xiaomei, CHEN Shanshan, CHEN Jiwei. Provable radio frequency identification authentication protocol with scalability [J]. Journal of Computer Applications, 2019, 39(3): 774-778.
[10]	LI Zhuhong, ZHAO Canming, YAN Long, ZHANG Xinming. Load balancing opportunistic routing protocol for power line communication network in smart grids [J]. Journal of Computer Applications, 2019, 39(3): 812-816.
[11]	SUN Huiming, DU Yuyue. Process model mining method for multi-concurrent 2-loops of triangles [J]. Journal of Computer Applications, 2019, 39(3): 851-857.
[12]	WANG Zewu, SUN Lei, GUO Songhui, SUN Ruichen. Scheduling method of virtual cipher machine based on entropy weight evaluation in cryptography cloud [J]. Journal of Computer Applications, 2018, 38(5): 1353-1359.
[13]	XIAO Wen, HU Juan. Performance analysis of frequent itemset mining algorithms based on sparseness of dataset [J]. Journal of Computer Applications, 2018, 38(4): 995-1000.
[14]	FU Chaojiang, WANG Tianqi, LIN Yuerong. Parallel algorithm for explicit finite element analysis based on efficient parallel computational strategy [J]. Journal of Computer Applications, 2018, 38(4): 1072-1077.
[15]	LU Liang, YU Jiong, BIAN Chen, YING Changtian, SHI Kangli, PU Yonglin. Task scheduling algorithm based on weight in Storm [J]. Journal of Computer Applications, 2018, 38(3): 699-706.