A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center


  • Shally Vats
  • Sanjay Kumar Sharma
  • Sunil Kumar




energy optimization, cloud data center, VM consolidation, energy efficiency


Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.


P. Mell and T. Grance, The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology, 2011. https://doi.org/10.6028/NIST.SP.800-145.

A. Tarafdar, M. Debnath, S. Khatua, and R. K. Das, “Energy and quality of service-aware virtual machine consolidation in a cloud data center,” J. Supercomput., vol. 76, pp. 9095–9126, 2020. https://doi.org/10.1007/s11227-020-03203-3

Gartner Report. [Online]. Available at: www.gartner.comitpage.jspid=1442113.

D. Rincón, J. F. Botero, F. Raspall, and D. Remondo, “A novel collaboration paradigm for reducing energy consumption and carbon dioxide emissions in data centres,” Comput. J., vol. 56, no. 12, pp. 1518–1536, 2013. https://doi.org/10.1093/comjnl/bxt053.

H. Goudarzi and M. Pedram, “Hierarchical SLA-driven resource management for peak power-aware and energy-efficient operation of a cloud datacenter,” IEEE Trans. Cloud Comput., vol. 4, no. 2, pp. 222–236, 2016. https://doi.org/10.1109/TCC.2015.2474369.

V. De Maio, R. Prodan, and S. Benedict, “Modelling energy consumption of network transfers and virtual machine migration,” Futur. Gener. Comput. Syst., vol. 56, pp. 388–406, 2016. https://doi.org/10.1016/j.future.2015.07.007.

W. Forrest, “How to cut data centre carbon emissions.” [Online]. Available: https://www.forbes.com/sites/benkepes/2015/06/03/30-of-servers-are-sitting-comatose-according-to-research/#1855464559c7.

Q. Zhou, J. Lou, and Y. Jiang, “Optimization of energy consumption of green data center in e-commerce,” Sustain. Comput. Informatics Syst., vol. 23, pp. 103–110, 2019. https://doi.org/10.1016/j.suscom.2019.07.008.

I. Ghani, N. Niknejad, and S. R. Jeong, “Energy saving in green cloud computing data centers : A review,” J. Theor. Appl. Inf. Technol., vol. 74, no. 1, pp. 16–30, 2015.

X. You, Y. Li, M. Zheng, C. Zhu, and L. Yu, “A survey and taxonomy of energy efficiency relevant surveys in cloud-related environments,” IEEE Access, vol. 5, pp. 14066–14078, 2017. https://doi.org/10.1109/ACCESS.2017.2718001.

Shally, S. K. Sharma, and S. Kumar, “Energy efficient resource management in cloud environment: Progress and challenges,” Proceedings of the 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2016, pp. 636–641. https://doi.org/10.1109/PDGC.2016.7913200.

K. H. Kim and R. Buyya, “Power-aware provisioning of cloud resources for real-time services,” Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science, 2009, pp. 1–6. https://doi.org/10.1145/1657120.1657121.

R. Nathuji and K. Schwan, “VirtualPower : Coordinated power management in virtualized enterprise systems,” ACM SIGOPS operating systems review, vol. 42, no. 3, pp. 48–59, 2008. https://doi.org/10.1145/1294261.1294287.

J. Xu and J. A. B. Fortes, “Multi-objective virtual machine placement in virtualized data center environments,” Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, 2010, pp. 179–188. https://doi.org/10.1109/GreenCom-CPSCom.2010.137.

H. N. Van and J. Menaud, “Performance and power management for cloud infrastructures,” Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing, 2010, pp. 329–336. https://doi.org/10.1109/CLOUD.2010.25.

A. Beloglazov and R. Buyya, “Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers,” Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e- Science, Bangalore, India, 2010, vol. 4, pp. 1-4. https://doi.org/10.1145/1890799.1890803.

A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers,” Concurr. Comput. Pract. Exp., vol. 24, no. 13, pp. 1397–1420, 2012. https://doi.org/10.1002/cpe.1867.

Y. Gao, H. Guan, Z. Qi, T. Song, F. Huan, and L. Liu, “Service level agreement based energy-efficient resource management in cloud data centers,” Comput. Electr. Eng., vol. 40, no. 5, pp. 1621–1633, 2014. https://doi.org/10.1016/j.compeleceng.2013.11.001.

M. Shojafar, N. Cordeschi, and E. Baccarelli, “Energy-efficient adaptive resource management for real-time vehicular cloud services,” IEEE Trans. Cloud Comput., vol. 7, no. 1, pp. 196–209, 2016. https://doi.org/10.1109/TCC.2016.2551747.

L. Zhang, Y. Wang, L. Zhu, and W. Ji, “Towards energy efficient cloud: an optimized ant colony model for virtual machine placement,” J. Commun. Inf. Networks, vol. 1, no. 4, pp. 116–132, 2016. https://doi.org/10.1007/BF03391585.

X. Liu, S. Member, Z. Zhan, and J. D. Deng, “An energy efficient ant colony system for virtual machine placement in cloud computing,” IEEE Trans. Evol. Comput., vol. 22, no. 1, pp. 113–128, 2016. https://doi.org/10.1109/TEVC.2016.2623803.

D. Bui, Y. Yoon, E. Huh, S. Jun, and S. Lee, “Energy efficiency for cloud computing system based on predictive optimization,” J. Parallel Distrib. Comput., vol. 102, pp. 103–114, 2017. https://doi.org/10.1016/j.jpdc.2016.11.011.

S. B. Shaw and J. P. Kumar, “Energy-performance trade-off through restricted virtual machine consolidation in cloud data center,” Proceedings of the IEEE International Conference on Intelligent Computing and Control (I2C2), 2017, pp. 1–6. https://doi.org/10.1109/I2C2.2017.8321783.

M. A. Haghighi, M. Maeen, and M. Haghparast, “An Energy‑efficient dynamic resource management approach based on clustering and meta‑heuristic algorithms in cloud computing IaaS platforms,” Wirel. Pers. Commun., vol. 4, pp. 1367–1391, 2019. https://doi.org/10.1007/s11277-018-6089-3.

T. Yang, Y. C. Lee, and A. Y. Zomaya, “Collective energy-efficiency approach to data center networks planning,” IEEE Trans. Cloud Comput., vol. 6, no. 3, pp. 656–666, 2015. https://doi.org/10.1109/TCC.2015.2511732.

R. Wang, R. Esteves, L. Shi, and J. A. Wickboldt, “Network-aware placement of virtual machine ensembles using effective bandwidth estimation,” Proceedings of the 10th International Conference on Network and Service Management (CNSM) and Workshop, 2014, pp. 100–108. https://doi.org/10.1109/CNSM.2014.7014146.

R. Wang et al., “Using empirical estimates of effective bandwidth in network-aware placement of virtual machines in datacenters,” IEEE Trans. Netw. Serv. Manag., vol. 13, no. 2, pp. 267–280, 2016. https://doi.org/10.1109/TNSM.2016.2530309.

T. Chen, X. Gao, and G. Chen, “Optimized virtual machine placement with traffic-aware balancing in data center networks,” Scientific Programming, vol. 2016, paper id 3101658, 2016. https://doi.org/10.1155/2016/3101658.

G. Xu, B. Dai, B. Huang, J. Yang, and S. Wen, “Bandwidth-aware energy efficient flow scheduling with SDN in data center networks,” Futur. Gener. Comput. Syst., vol. 68, pp. 163-174, 2016. https://doi.org/10.1016/j.future.2016.08.024.

A. Marotta, S. Avallone, and A. Kassler, “A joint power efficient server and network consolidation approach for virtualized data centers,” Comput. Networks, vol. 130, pp. 65–80, 2018. https://doi.org/10.1016/j.comnet.2017.11.003.

M. Al-Fares and A. Loukissas, “A scalable, commodity data center network architecture,” ACM SIGCOMM Comput. Commun. Rev., vol. 38, no. 4, pp. 63–74, 2008. https://doi.org/10.1145/1402946.1402967.

D. Kliazovich, P. Bouvry, and S. Ullah, “DENS : data center energy-efficient network-aware scheduling,” Cluster Comput., vol. 16, no. 1, pp. 65–75, 2013. https://doi.org/10.1007/s10586-011-0177-4.

T. Yang, H. Pen, W. Li, and A. Y. Zomaya, “An energy-efficient virtual machine placement and route scheduling scheme in data center networks,” Futur. Gener. Comput. Syst., vol. 77, pp. 1–11, 2017. https://doi.org/10.1016/j.future.2017.05.047.

P. Reviriego, V. Sivaraman, Z. Zhao, J. A. Maestro, and A. Vishwanath, “An energy consumption model for energy efficient ethernet switches,” Proceedings of the 2012 International Conference on High Performance Computing & Simulation (HPCS), 2012, pp. 98–104. https://doi.org/10.1109/HPCSim.2012.6266897.

Shally, S. K. Sharma, and S. Kumar, “A dynamic threshold based energy efficient method for cloud datacenters,” Int. J. Softw. Innov., pp. 54–67, 2020. https://doi.org/10.4018/IJSI.2020040104.

R. N. Calheiros, R. Ranjan, A. Beloglazov, and A. F. De Rose, “CloudSim : a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Softw. Pract. Exp., vol. 41, no. 1, pp. 23–50, 2011. https://doi.org/10.1002/spe.995.

K. Park and V. S. Pai, “CoMon : A Mostly-Scalable Monitoring System for PlanetLab,” ACM SIGOPS Oper. Syst. Rev., vol. 40, no. 1, pp. 65–74, 2006. https://doi.org/10.1145/1113361.1113374.

Shally, S. K. Sharma, and S. Kumar, “Measuring energy efficiency of cloud datacenters,” Int. J. Recent Technol. Eng., no. 3, pp. 5428–5433, 2019. https://doi.org/10.35940/ijrte.B3548.098319.




How to Cite

Vats, S., Sharma, S. K., & Kumar, S. (2021). A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center. International Journal of Computing, 20(1), 85-91. https://doi.org/10.47839/ijc.20.1.2103