Energy-aware fairness enhanced resource scheduling method in cloud environment

doi:10.11772/j.issn.1001-9081.2016.10.2692

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (10): 2692-2697.DOI: 10.11772/j.issn.1001-9081.2016.10.2692

Previous Articles Next Articles

Energy-aware fairness enhanced resource scheduling method in cloud environment

XUE Shengjun^1,2, QIU Shuang^1,2, XU Xiaolong³

1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
2. Jiangsu Engineering Center of Network Monitoring (Nanjing University of Information Science and Technology), Nanjing Jiangsu 210044, China;
3. State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing Jiangsu 210023, China

Received:2016-04-14 Revised:2016-05-26 Online:2016-10-10 Published:2016-10-10
Supported by:
BackgroundThis work is partially supported by the National Natural Science Foundation of China (41275116).

云环境下能耗感知的公平性提升资源调度策略

薛胜军^1,2, 邱爽^1,2, 许小龙³

1. 南京信息工程大学计算机与软件学院, 南京 210044;
2. 江苏省网络监控中心(南京信息工程大学), 南京 210044;
3. 计算机软件新技术国家重点实验室(南京大学), 南京 210023

通讯作者: 邱爽,E-mail:13813953451@163.com
作者简介:薛胜军(1956—),男,山东青岛人,教授,博士生导师,博士,CCF会员,主要研究方向:计算机网络、云计算、应用气象;邱爽(1992—),女,江苏南京人,硕士研究生,主要研究方向:云计算、资源调度;许小龙(1988—),男,江苏海安人,博士研究生,主要研究方向:云计算、绿色计算。
基金资助:
国家自然科学基金资助项目（41275116）。

Abstract

Abstract: To address the problems of large energy consumption and illegal possession of computing resources by users in cloud environment, a new algorithm named Fair and Green Resource Scheduling Algorithm (FGRSA) was proposed to save resources and enhance the fairness of the system, so that all users can reasonably use all the resources in the data center. By using the proposed method, various types of resources can be can scheduled to make use of all resources to achieve relative fairness. The simulation experiments of the proposed scheduling strategy was conducted on CloudSim. Experimental resutls show that, compared with Greedy algorithm and Round Robin algorithm, FGRSA can significantly reduce the energy consumption and simultaneously ensure fair use of all types of resources.

Key words: cloud computing, resource scheduling, energy consumption model, fairness, multi-type resources

摘要： 针对云计算环境下大量的能源消耗和一些用户非法占有计算资源的问题，为了节约资源，同时提高系统的公平性，使所有用户能合理地使用数据中心的所有资源，提出一种基于能耗感知的节能公平的资源调度算法（FGRSA）。该算法能高效调度各种类型的资源使所有资源使用量达到一个相对的公平。通过CloudSim仿真平台，对提出的调度策略进行仿真实验。实验结果表明，与Greedy算法和Round Robin算法相比，FGRSA能够在大幅度降低系统能耗的同时，保证各类型资源的公平使用。

关键词: 云计算, 资源调度, 能耗模型, 公平性, 多类型资源

CLC Number:

TP338.6

XUE Shengjun, QIU Shuang, XU Xiaolong. Energy-aware fairness enhanced resource scheduling method in cloud environment[J]. Journal of Computer Applications, 2016, 36(10): 2692-2697.

薛胜军, 邱爽, 许小龙. 云环境下能耗感知的公平性提升资源调度策略[J]. 计算机应用, 2016, 36(10): 2692-2697.

References

[1] 刘鹏.云计算[M]. 2版.北京:电子工业出版社, 2011: 2-3.(LIU P. Cloud Computing[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2011: 2-3.)
[2] COOK G, VAN HOM J. How dirty is your data? A look at the energy choices that power cloud computing [EB/OL]. [2015-03-10]. http://www.greenpeace.org/international/en/publications/reports/How-dirty-is-your-data/.
[3] LAGO D G D, MADEIRA E R M, BITTENCOURT L F. Power-aware virtual machine scheduling on clouds using active cooling control and DVFS[C]//Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science. New York: ACM, 2011: Article No. 2.
[4] JUVE G, CHERVENAK A, DEELMAN E, et al. Characterizing and profiling scientific workflows[J]. Future Generation Computer Systems, 2013, 29(3):682-692.
[5] 卢笛, 马建峰, 王一川, 等. 云计算下保障公平性的多资源分配算法[J]. 西安电子科技大学学报(自然科学版), 2014,41(3):162-168.(LU D, MA J F, WANG Y C, et al. Enhanced fairness-based multi-resource allocation algorithm for cloud computing[J]. Journal of Xidian University (Natural Science), 2014,41(3):162-168.)
[6] 邓维, 郝沁汾, 金海.云计算数据中心的新能源应用:研究现状与趋势[J]. 计算机学报, 2013, 36(3):582-96.(DENG W, HAO Q F, JIN H. Leveraging renewable energy in cloud computing datacenters: state of the art and future research[J]. Chinese Journal of Computers, 2013, 36(3):582-96.)
[7] JUNG G, HILTUNEN M A, JOSHI K R, et al. Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures[C]//Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems. Piscataway, NJ: IEEE, 2010:62-73.
[8] LEE Y C, ZOMAYA A Y. Energy efficient utilization of resources in cloud computing systems[J]. Journal of Supercomputing, 2010, 60(2):268-280.
[9] LIU L, WANG H, LIU X, et al. GreenCloud: a new architecture for green data center[C]//Proceedings of the 6th International Conference Industry Session on Autonomic Computing and Communications Industry Session. New York: ACM, 2009:29-38.
[10] KANSAL A, ZHAO F, LIU J, et al. Virtual machine power metering and provisioning[C]//Proceedings of the 1st ACM Symposium on Cloud Computing. New York: ACM, 2010:39-50.
[11] SCHILL A. Power consumption estimation models for processors, virtual machines, and servers[J]. IEEE Transactions on Parallel & Distributed Systems, 2014, 25(6):1600-1614.
[12] BOHRA A E H, CHAUDHARY V. VMeter: power modelling for virtualized clouds[C]//Proceedings of the 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum. Piscataway, NJ: IEEE, 2010: 1-8.
[13] MEZMAZ M, MELAB N, KESSACI Y, et al. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems[J]. Journal of Parallel & Distributed Computing, 2011, 71(11):1497-1508.
[14] NATHUJI R, SCHWAN K. VirtualPower: coordinated power management in virtualized enterprise systems[C]//Proceedings of the 2007 ACM Symposium on Operating Systems Principles. New York: ACM, 2007:265-278.
[15] BELOGLAZOV A, ABAWAJY J, BUYYA R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J]. Future Generation Computer Systems, 2012, 28(5):755-768.
[16] BELOGLAZOV A, BUYYA R. Energy efficient resource management in virtualized cloud data centers[C]//CCGRID 2010: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. Washington, DC: IEEE Computer Society, 2010: 826-831.
[17] ZHU D, MOSSE D, MELHEM R. Multi-resource periodic scheduling problem: how much fairness is necessary? [C]//Proceedings of the 24th IEEE International Real-Time Systems Symposium. Piscataway, NJ: IEEE, 2003: 142-151.
[18] GHODSI A, ZAHARIA M, HINDMAN B, et al. Dominant resource fairness: fair allocation of multiple resource types[C]//Proceedings of the 8th USENIX Conference on NSDI. Berkeley: USENIX, 2011: 24-37.
[19] CALHEIROS R N, RANJAN R, BELOGLAZOV A, et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software Practice & Experience, 2011, 41(1):23-50.
[20] XU X, DOU W, ZHANG X, et al. EnReal: an energy-aware resource allocation method for scientific workflow executions in cloud environment[J]. IEEE Transactions on Cloud Computing, 2016, 4(2): 166-179.
[21] GUOY, STOLYAR A L, WALID A. Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud[C]//Proceedings of IEEE INFOCOM 2013. Piscataway, NJ: IEEE, 2013: 620-628.
[22] LI X, WU J, TANG S, et al. Let's stay together: towards traffic aware virtual machine placement in data centers[C]//Proceedings of the IEEE INFOCOM 2014. Piscataway, NJ: IEEE, 2014:1842-1850.

Energy-aware fairness enhanced resource scheduling method in cloud environment

云环境下能耗感知的公平性提升资源调度策略

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Haixin HUANG, Guangwei YU, Shoushan CHENG, Chunming LI. Full coverage path planning of bridge inspection wall-climbing robot based on improved grey wolf optimization [J]. Journal of Computer Applications, 2024, 44(3): 966-971.
[2]	Jiaxing LU, Hua DAI, Yuanlong LIU, Qian ZHOU, Geng YANG. Dictionary partition vector space model for ciphertext ranked search in cloud environment [J]. Journal of Computer Applications, 2023, 43(7): 1994-2000.
[3]	Kun YOU, Qinhui WANG, Xin LI. General multi-unit false-name-proof auction mechanism for cloud computing [J]. Journal of Computer Applications, 2023, 43(11): 3351-3357.
[4]	Jingyu SUN, Jiayu ZHU, Ziqiang TIAN, Guozhen SHI, Chuanjiang GUAN. Attribute based encryption scheme based on elliptic curve cryptography and supporting revocation [J]. Journal of Computer Applications, 2022, 42(7): 2094-2103.
[5]	Jinquan ZHANG, Shouwei XU, Xincheng LI, Chongyang WANG, Jingzhi XU. Cloud computing task scheduling based on orthogonal adaptive whale optimization [J]. Journal of Computer Applications, 2022, 42(5): 1516-1523.
[6]	CHEN Jiahao, YIN Xinchun. Traceable and revocable ciphertext-policy attribute-based encryption scheme based on cloud-fog computing [J]. Journal of Computer Applications, 2021, 41(6): 1611-1620.
[7]	GE Lina, HU Yugu, ZHANG Guifen, CHEN Yuanyuan. Reverse hybrid access control scheme based on object attribute matching in cloud computing environment [J]. Journal of Computer Applications, 2021, 41(6): 1604-1610.
[8]	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.
[9]	Xiaoling SUN, Guang YANG, Yanping SHEN, Qiuge YANG, Tao CHEN. Searchable encryption scheme based on splittable inverted index [J]. Journal of Computer Applications, 2021, 41(11): 3288-3294.
[10]	LYU Jiayu, ZHU Zhirong, YAO Zhiqiang. Two-channel dynamic data encryption strategy in cloud computing environment [J]. Journal of Computer Applications, 2020, 40(8): 2268-2273.
[11]	GUO Shujie, LI Zhihua, LIN Kaiqing. Fuzzy membership degree based virtual machine placement algorithmin cloud environment [J]. Journal of Computer Applications, 2020, 40(5): 1374-1381.
[12]	CHEN Chengjun, MAO Yingchi, WANG Yichao. CNN model compression based on activation-entropy based layer-wise iterative pruning strategy [J]. Journal of Computer Applications, 2020, 40(5): 1260-1265.
[13]	XU Yingxin, SUN Lei, ZHAO Jiancheng, GUO Songhui. Virtual field programmable gate array placement strategy based on ant colony optimization algorithm [J]. Journal of Computer Applications, 2020, 40(3): 747-752.
[14]	Fuxin LIU, Jingwei LI, Yihong WANG, Lin LI. Design and implementation of cloud native massive data storage system based on Kubernetes [J]. Journal of Computer Applications, 2020, 40(2): 547-552.
[15]	Qingyong WANG, Yingchi MAO, Yichao WANG, Longbao WANG. Computing task offloading based on multi-cloudlet collaboration [J]. Journal of Computer Applications, 2020, 40(2): 328-334.