article

VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

Authors:

Xiangmin Liang,

Luca Chiaraviglio,

Naixue XiongAuthors Info & Claims

Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 57, Issue 1

Pages 179 - 196

https://doi.org/10.1016/j.comnet.2012.09.008

Published: 01 January 2013 Publication History

Abstract

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.

References

[1]

Krishna, Kant, Data center evolution: a tutorial on state of the art, issues, and challenges. Computer Networks. v53. 2939-2965.

Digital Library

[2]

EPA, Report to Congress on Server and Data Center Energy Efficiency, Technical report, US Environmental Protection Agency, 2007.

[3]

Greenberg, A., Hamilton, J., Maltz, D.A. and Patel, P., The cost of a cloud: research problems in data center networks. SIGCOMM Computer Communication Review. v39. 68-73.

Digital Library

[4]

Barroso, L.A. and Hölzle, U., The case for energy-proportional computing. Computer. v40. 33-37.

Digital Library

[5]

S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, D. Wetherall, Reducing network energy consumption via sleeping and rate-adaptation, in: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, NSDI'08, USENIX Association, Berkeley, CA, USA, 2008, pp. 323-336.

Digital Library

[6]

Herrería-Alonso, S., Rodríguez-Pérez, M., Fernández-Veiga, M. and López-García, C., Optimal configuration of energy-efficient ethernet. Computer Networks. v56. 2456-2467.

Digital Library

[7]

A. Bianzino, C. Chaudet, F. Larroca, D. Rossi, J. Rougier, Energy-aware routing: a reality check, in: Proceedings of the 2010 IEEE GLOBECOM Workshops, GC Wkshps'10, pp. 1422-1427.

[8]

Bolla, R., Bruschi, R., Davoli, F. and Cucchietti, F., Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Communications Surveys Tutorials. v13. 223-244.

[9]

Gupta, M. and Singh, S., Greening of the internet. In: SIGCOMM'03, ACM, New York, NY, USA. pp. 19-26.

Digital Library

[10]

S. Nedevschi, J. Chandrashekar, J. Liu, B. Nordman, S. Ratnasamy, N. Taft, Skilled in the art of being idle: reducing energy waste in networked systems, in: Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI'09, USENIX Association, Berkeley, CA, USA, 2009, pp. 381-394.

Digital Library

[11]

Chiaraviglio, L., Mellia, M. and Neri, F., Reducing power consumption in backbone networks. In: Proceedings of the 2009 IEEE International Conference on Communications, ICC'09, IEEE Press, Piscataway, NJ, USA. pp. 2298-2303.

Digital Library

[12]

B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, N. McKeown, Elastictree: saving energy in data center networks, in: Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI'10, USENIX Association, Berkeley, CA, USA, 2010, pp. 1-16.

Digital Library

[13]

Saran, H. and Vazirani, V.V., Finding k-cuts within twice the optimal. SIAM Journal of Computing. v24. 101-108.

Digital Library

[14]

Taillard, E., Robust taboo search for the quadratic assignment problem. Parallel Computing. v17. 443-455.

Digital Library

[15]

M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, A. Vahdat, Hedera: dynamic flow scheduling for data center networks, in: Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI'10, USENIX Association, Berkeley, CA, USA, 2010, pp. 19-19.

Digital Library

[16]

F. Liu, B. Zhang, K. Miao, J. He, X. Wu, W. Mao, A Cloud Test Bed for China Railway Enterprise Data Center, Technical report, Intel Corporation, 2009.

[17]

Greenberg, A., Hamilton, J.R., Jain, N., Kandula, S., Kim, C., Lahiri, P., Maltz, D.A., Patel, P. and Sengupta, S., Vl2: a scalable and flexible data center network. In: Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication, SIGCOMM'09, ACM, New York, NY, USA. pp. 51-62.

Digital Library

[18]

Zhang, Y., Su, A.-J. and Jiang, G., Understanding data center network architectures in virtualized environments: a view from multi-tier applications. Computer Networks. v55. 2196-2208.

Digital Library

[19]

A scalable, commodity data center network architecture. In: Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication, SIGCOMM'08, ACM, New York, NY, USA. pp. 63-74.

Digital Library

[20]

Clos, C., A study of non-blocking switching networks. Bell System Technical Journal. v32. 406-424.

[21]

Challenges towards elastic power management in internet data centers. In: Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW'09, IEEE Computer Society, Washington, DC, USA. pp. 65-72.

Digital Library

[22]

Meijer, G.I., Cooling energy-hungry data centers. Science. v328. 318-319.

[23]

Fakhim, B., Behnia, M., Armfield, S. and Srinarayana, N., Cooling solutions in an operational data centre: a case study. Applied Thermal Engineering. v31. 2279-2291.

[24]

Hou, W., Guo, L., Wei, X. and Gong, X., Multi-granularity and robust grooming in power- and port-cost-efficient ip over WDM networks. Computer Networks. v56. 2383-2399.

Digital Library

[25]

Cuomo, F., Cianfrani, A., Polverini, M. and Mangione, D., Network pruning for energy saving in the internet. Computer Networks. v56. 2355-2367.

Digital Library

[26]

Shang, Y., Li, D. and Xu, M., Energy-aware routing in data center network. In: Proceedings of the First ACM SIGCOMM Workshop on Green Networking, Green Networking'10, ACM, New York, NY, USA. pp. 1-8.

Digital Library

[27]

Mahadevan, P., Banerjee, S., Sharma, P., Shah, A. and Ranganathan, P., On energy efficiency for enterprise and data center networks. IEEE Communications Magazine. v49. 94-100.

[28]

F. Machida, D.S. Kim, J.S. Park, K. Trivedi, Toward optimal virtual machine placement and rejuvenation scheduling in a virtualized data center, in: Proceedings of the 2008 IEEE International Conference on Software Reliability Engineering Workshops, ISSRE Wksp'08, pp. 1-3.

[29]

A. Kochut, On impact of dynamic virtual machine reallocation on data center efficiency, in: Proceedings of the 2008 IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems, MASCOTS'08, pp. 1-8.

[30]

Meng, X., Pappas, V. and Zhang, L., Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceedings of the 29th IEEE Conference on Information Communications, INFOCOM'10, IEEE Press, Piscataway, NJ, USA. pp. 1154-1162.

Digital Library

[31]

R. McGeer, P. Mahadevan, S. Banerjee, On the complexity of power minimization schemes in data center networks, in: Proceedings of the 2010 IEEE Global Telecommunications Conference, GLOBECOM'10, pp. 1-5.

[32]

Mann, V., Kumar, A., Dutta, P. and Kalyanaraman, S., Vmflow: leveraging VM mobility to reduce network power costs in data centers. In: vol. Part I: NETWORKING'11, Springer-Verlag, BerlinHeidelberg. pp. 198-211.

Digital Library

[33]

Benson, T., Anand, A., Akella, A. and Zhang, M., Understanding data center traffic characteristics. SIGCOMM Computer Communication Review. v40. 92-99.

Digital Library

[34]

Garey, M.R. and Johnson, D.S., Computers and Intractability: A Guide to the Theory of NP-Completeness. 1979. W.H. Freeman.

Digital Library

[35]

Gomory, R.E. and Hu, T.C., Multi-terminal network flows. Journal of the Society for Industrial and Applied Mathematics. v9. 551-570.

[36]

Glover, F., Tabu search: a tutorial. Interfaces. v20. 74-94.

[37]

Gude, N., Koponen, T., Pettit, J., Pfaff, B., Casado, M., McKeown, N. and Shenker, S., Nox: towards an operating system for networks. SIGCOMM Computer Communication Review. v38. 105-110.

Digital Library

[38]

Milojičić, D., Llorente, I.M. and Montero, R.S., Opennebula: a cloud management tool. IEEE Internet Computing. v15. 11-14.

Digital Library

[39]

Gyarmati, L. and Trinh, T.A., Scafida: a scale-free network inspired data center architecture. SIGCOMM Computer Communication Review. v40. 4-12.

Digital Library

[40]

D. Kliazovich, P. Bouvry, Y. Audzevich, S. Khan, Greencloud: a packet-level simulator of energy-aware cloud computing data centers, in: Proceedings of the 2010 IEEE Global Telecommunications Conference, GLOBECOM'10, pp. 1-5.

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cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 57, Issue 1

January, 2013

374 pages

ISSN:1389-1286

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Copyright © Elsevier B.V. © 2012.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 January 2013

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Mingxun ZJiewu YZhigang MYanping W(2023)Deep Forest-Based E-Commerce Recommendation Attack Detection ModelSecurity and Communication Networks10.1155/2023/84132472023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8413247
Rahman FNewaz SAu TSuhaili WMahmud MLee G(2022)EnTruVeFuture Generation Computer Systems10.1016/j.future.2021.07.036126:C(196-210)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.future.2021.07.036
Kiani MKhayyambashi M(2021)A network-aware and power-efficient virtual machine placement scheme in cloud datacenters based on chemical reaction optimizationComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2021.108270196:COnline publication date: 4-Sep-2021
https://dl.acm.org/doi/10.1016/j.comnet.2021.108270
Lu JZhao WZhu HLi JCheng ZXiao G(2021)Optimal machine placement based on improved genetic algorithm in cloud computingThe Journal of Supercomputing10.1007/s11227-021-03953-878:3(3448-3476)Online publication date: 26-Jul-2021
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Feng HDeng YZhou YDi Natale GFummi F(2020)A heat-recirculation-aware VM placement strategy for data centersProceedings of the 23rd Conference on Design, Automation and Test in Europe10.5555/3408352.3408494(626-629)Online publication date: 9-Mar-2020
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Al-Tarazi MChang JChang MLo DGamess E(2020)Prediction-Based Joint Energy Optimization for Virtualized Data CentersProceedings of the 2020 ACM Southeast Conference10.1145/3374135.3385279(160-167)Online publication date: 2-Apr-2020
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Rashida SSabaei MEbadzadeh MRahmani A(2020)A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environmentCluster Computing10.1007/s10586-019-02956-823:2(797-836)Online publication date: 1-Jun-2020
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Talebian HGani ASookhak MAbdelatif AYousafzai AVasilakos AYu F(2020)Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issuesCluster Computing10.1007/s10586-019-02954-w23:2(837-878)Online publication date: 1-Jun-2020
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Jayanetti ABuyya RJohnson KSpillner JKlusáček DAnjum A(2019)J-OPT: A Joint Host and Network Optimization Algorithm for Energy-Efficient Workflow Scheduling in Cloud Data CentersProceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing10.1145/3344341.3368822(199-208)Online publication date: 2-Dec-2019
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