Power savings in software defined data center networks via modified hybrid genetic algorithm

doi:10.1016/S1005-8885(17)60226-7

中国邮电高校学报(英文) ›› 2017, Vol. 24 ›› Issue (4): 76-86.doi: 10.1016/S1005-8885(17)60226-7

• Others • 上一篇

Power savings in software defined data center networks via modified hybrid genetic algorithm

谢坤¹,黄小红¹,马懋德²,张沛

收稿日期:2017-03-13 修回日期:2017-06-28 出版日期:2017-08-30 发布日期:2017-08-30
通讯作者: 黄小红 E-mail:huangxh@bupt.edu.cn
基金资助:
NSFC-新疆联合基金资助项目;国家国际科技合作计划

Power savings in software defined data center networks via modified hybrid genetic algorithm

Received:2017-03-13 Revised:2017-06-28 Online:2017-08-30 Published:2017-08-30
Contact: Xiao-Hong Huang E-mail:huangxh@bupt.edu.cn
Supported by:
Joint Funds of National Natural Science Foundation of China and Xinjiang;International Science and Technology Cooperation and Exchange Project of China

摘要/Abstract

摘要： In modern data centers, power consumed by network is an observable portion of the total energy budget and thus improving the energy efficiency of data center networks (DCNs) truly matters. One effective way for this energy efficiency is to make the size of DCNs elastic along with traffic demands by flow consolidation and bandwidth scheduling, i.e., turning off unnecessary network components to reduce the power consumption. Meanwhile, having the instinct support for data center management, software defined networking (SDN) provides a paradigm to elastically control the resources of DCNs. To achieve such power savings, most of the prior efforts just adopt simple greedy heuristic to reduce computational complexity. However, due to the inherent problem of greedy algorithm, a good-enough optimization cannot be always guaranteed. To address this problem, a modified hybrid genetic algorithm (MHGA) is employed to improve the solution’s accuracy, and the fine-grained routing function of SDN is fully leveraged. The simulation results show that more efficient power management can be achieved than the previous studies, by increasing about 5% of network energy savings.

关键词: data center networks, energy efficiency, software defined networking, elastic topology, genetic algorithm

Abstract: In modern data centers, power consumed by network is an observable portion of the total energy budget and thus improving the energy efficiency of data center networks (DCNs) truly matters. One effective way for this energy efficiency is to make the size of DCNs elastic along with traffic demands by flow consolidation and bandwidth scheduling, i.e., turning off unnecessary network components to reduce the power consumption. Meanwhile, having the instinct support for data center management, software defined networking (SDN) provides a paradigm to elastically control the resources of DCNs. To achieve such power savings, most of the prior efforts just adopt simple greedy heuristic to reduce computational complexity. However, due to the inherent problem of greedy algorithm, a good-enough optimization cannot be always guaranteed. To address this problem, a modified hybrid genetic algorithm (MHGA) is employed to improve the solution’s accuracy, and the fine-grained routing function of SDN is fully leveraged. The simulation results show that more efficient power management can be achieved than the previous studies, by increasing about 5% of network energy savings.

Key words: data center networks, energy efficiency, software defined networking, elastic topology, genetic algorithm

中图分类号:

TP393

参考文献

1. ICTresearch. Beijing, China. 2013 (in Chinese).

2. Greenberg A, Hamilton J, Maltz D A, et al. The cost of a cloud: research problems in data center networks. ACM SIGCOMM Computer Communication Review, 2009, 39(1): 68-73

3. Heller B, Seetharaman S, Mahadevan P, et al. ElasticTree: saving energy in data center networks. Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation (NSDI’10), Apr 28-30, 2010, San Jose, CA, USA. Berkeley, CA, USA: USENIX Association, 2010: Article 17

4. Shang Y F, Li D, Xu M W. Energy-aware routing in data center network. Proceedings of the 1st ACM SIGCOMM Workshop on Green Networking, Aug 30, 2010, New Delhi, India. New York, NY, USA: ACM, 2010: 8p

5. Software-defined networking: the new norm for networks. White Paper. Palo Alto, CA, USA: Open Nerworking Fundation (ONF), 2012

6. OpenFlow switch specification, Version 1.5.1. ONF TS-050. Palo Alto, CA, USA: Open Nerworking Fundation (ONF), 2015

7. Tu R, Wang X, Yang Y J. Energy-saving model for SDN data centers. Journal of Supercomputing, 2014, 70(3): 1477-1495

8. Li D, Shang Y F, Chen C J. Software defined green data center network with exclusive routing. Proceedings of the 2014 IEEE Conference on Computer Communications (INFOCOM’14), Apr 27-May 2, 2014, Toronto, Canada. Piscataway, NJ, USA: IEEE, 2014: 1743-1751

9. Li D, Yu Y R, He W, et al. Willow: saving data center network energy for network-limited flows. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(9): 2610-2620

10. Wang X D, Wang X R, Zheng K Y, et al. Correlation-aware traffic consolidation for power optimization of data center networks. IEEE Transactions on Parallel and Distributed Systems, 2015, 27(4): 992-1006

11. Jiang H P, Chuck D, Chen W M. Energy-aware data center networks. Journal of Network and Computer Applications, 2016, 68: 80-89

12. Xu G, Dai B, Huang B X, et al. Bandwidth-aware energy efficient flow scheduling with SDN in data center networks. Future Generation Computer Systems, 2017, 68: 163-174

13. Rodrigues B B, Riekstin A C, Januario G C, et al. GreenSDN: bringing energy efficiency to an SDN emulation environment. Proceedings of the 14th IFIP/IEEE International Symposium on Integrated Network Management (IM’15), May 11-15, 2015, Ottawa, Canada. Piscataway, NJ, USA: IEEE, 2015: 948-953

14. Zhu H, Liao X K, de Laat C, et al. Joint flow routing-scheduling for energy efficient software defined data center networks: a prototype of energy-aware network management platform. Journal of Network and Computer Applications, 2016, 63(C): 110-124

15. Xie K, Huang X H, Hao S, et al. E³MC: improving energy efficiency via elastic multi-controller SDN in data center networks. IEEE Access, 2016, 4: 6780-6791

16. Al-Fares M, Loukissas A, Vahdat A. A scalable, commodity data center network architecture. Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication (SIGCOMM’08), Aug 17-22, 2008, Seattle, WA, USA. New York, NY, USA: ACM, 2008: 63-74

17. Greenberg A, Hamilton J R, Jain N, et al. VL2: a scalable and flexible data center network. Proceedings of the ACM SIGCOMM 2009 Conference on Data communication (SIGCOMM’09), Aug 16-21, 2009, Barcelona, Spain. New York, NY, USA: ACM, 2009: 51-62

18. Guo C X, Wu H T, Tan K, et al. DCell: a scalable and fault-tolerant network structure for data centers. Proceedings of the ACM SIGCOMM 2008 Conference on Data communication (SIGCOMM’08), Aug 17-22, 2008, Seattle, WA, USA. New York, NY, USA: ACM, 2008: 75-86

19. Guo C X, Lu G H, Li D, et al. BCube: a high performance, server-centric network architecture for modular data centers. Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication (SIGCOMM’09), Aug 16-21, 2009, Barcelona, Spain. New York, NY, USA: ACM, 2009: 63-74

20. Mahadevan P, Sharma P, Banerjee S, et al. A power benchmarking framework for network devices. Proceedings of the 8th International IFIP-TC 6 Networking Conference (Networking’09), May 11-15, 2009, Aachen, Germany. LNCS 5550. Berlin, Germany: Springer-Verlag, 2009: 795-808

21. Al-Fares M, Radhakrishnan S, Raghavan B, et al. Hedera: dynamic flow scheduling for data center networks. Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation (NSDI’10), Apr 28-30, 2010, San Jose, CA, USA. Berkeley, CA, USA: USENIX Association, 2010: Article 19

22. Ahuja R K, Magnanti T L, Orlin J B. Network flows: theory, algorithms, and applications. Englewood Cliffs, NJ, USA: Prentice Hall, 1993

23. Giroire F, Mazauric D, Moulierac J, et al. Minimizing routing energy consumption: from theoretical to practical results. Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications (GreenCom’10), and Cyber, Physical and Social Computing (CPSCom’10), Dec 18-20, 2010, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2010: 252-259

24. Benson T, Akella A, Maltz D A. Network traffic characteristics of data centers in the wild. Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (IMC’10), Nov 1-3, 2010, Melbourne, Australia. New York, NY, USA: ACM, 2010: 267-280

[1]	Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin. Prediction based dynamic resource allocation method for edge computing first networking [J]. 中国邮电高校学报(英文版), 2023, 30(3): 78-87.
[2]	Chen Hui, Jiang Xiaoling, Wu Tianting, Mou Xingyu. Crowd sensing data delivery based on tangle DAG network [J]. 中国邮电高校学报(英文版), 2023, 30(3): 88-98.
[3]	Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao. Reinforced virtual optical network embedding algorithm in EONs for edge computing [J]. 中国邮电高校学报(英文版), 2022, 29(6): 18-29.
[4]	赵国生刘冬梅王健. Cloud security situation prediction method based on grey wolf optimization and BP neural network [J]. 中国邮电高校学报(英文版), 2020, 27(6): 30-41.
[5]	刘琨, 王辉, 申自浩. Prediction of network attack profit path based on NAPG model[J]. 中国邮电高校学报(英文版), 2020, 27(5): 91-102.
[6]	Li Bo, Sun Xuehong, Li Chunshu, Xue Kaiping, Zhang Xiaoguang. Downlink energy efficiency modeling and optimization with backhaul awareness and interference price in ultra cellular HetNet[J]. 中国邮电高校学报(英文版), 2019, 26(2): 67-81.
[7]	Zhang Junsong, Jiang Yongcong, Gan Yong, Zhang Qikun. Bilinear pair based authentication protocol for wireless medical sensor network[J]. 中国邮电高校学报(英文版), 2018, 25(2): 28-38.
[8]	Yang Jingmin1, Zhang Wenjie1, Zou Fumin2. Survey of outdoor and indoor architecture design in TVWS networks[J]. 中国邮电高校学报(英文版), 2017, 24(6): 24-38.
[9]	Wu Zhijun,Cui Zihan, Wang Caiyun, Lei Jin. Access control scheme with attribute revocation for SWIM[J]. 中国邮电高校学报(英文版), 2017, 24(6): 49-54.
[10]	Yao Yukun, Liu Jiangbing, Xu Dongliang, Ren Zhi, Hu Qing. Centralized congestion control routing protocol based on multi-metrics for low power and lossy networks[J]. 中国邮电高校学报(英文版), 2017, 24(5): 35-43.
[11]	Tian Shengwen, Wang Jingyu, Yang Hongyong, Cui Guanghai. Stateless overlay multicast with in-packet bloom filters[J]. 中国邮电高校学报(英文版), 2017, 24(5): 44-52.
[12]	Tan Xiaoying, Huang Dan, Guo Yuchun, Chen Changjia. Dynamic resource allocation in cloud download service[J]. 中国邮电高校学报(英文版), 2017, 24(5): 53-59.
[13]	李文璟喻鹏王瑞一丰雷董欧洲邱雪松. Quality of experience evaluation of HTTP video streaming based on user interactive behaviors[J]. 中国邮电高校学报(英文版), 2017, 24(3): 24-32.
[14]	邵彩幸张建华. Research of the multi-way connectivity probability for platoon-based Vehicle-to-Infrastructure communication network[J]. 中国邮电高校学报(英文版), 2016, 23(1): 1-7.
[15]	肖雅郑世慧孙斌. Trusted GPSR protocol without reputation faking in VANET[J]. Acta Metallurgica Sinica(English letters), 2015, 22(5): 22-31.

Power savings in software defined data center networks via modified hybrid genetic algorithm

Power savings in software defined data center networks via modified hybrid genetic algorithm

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

本文评价