Abstract
The increasing popularity of Software-Defined Network technologies is shaping the characteristics of present and future data centers. This trend, leading to the advent of Software-Defined Data Centers, will have a major impact on the solutions to address the issue of reducing energy consumption in cloud systems. As we move towards a scenario where network is more flexible and supports virtualization and softwarization of its functions, energy management must take into account not just computation requirements but also network related effects, and must explicitly consider migrations throughout the infrastructure of Virtual Elements (VEs), that can be both Virtual Machines and Virtual Routers. Failing to do so is likely to result in a sub-optimal energy management in current cloud data centers, that will be even more evident in future SDDCs. In this chapter, we propose a joint computation-plus-communication model for VEs allocation that minimizes energy consumption in a cloud data center. The model contains a threefold contribution. First, we consider the data exchanged between VEs and we capture the different connections within the data center network. Second, we model the energy consumption due to VEs migrations considering both data transfer and computational overhead. Third, we propose a VEs allocation process that does not need to introduce and tune weight parameters to combine the two (often conflicting) goals of minimizing the number of powered-on servers and of avoiding too many VE migrations. A case study is presented to validate our proposal. We apply our model considering both computation and communication energy contributions even in the migration process, and we demonstrate that our proposal outperforms the existing alternatives for VEs allocation in terms of energy reduction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
References
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput. Pract. Exp. 24(13), 1397–1420 (2012)
Canali, C., Lancellotti, R.: Exploiting classes of virtual machines for scalable IaaS cloud management. In: Proceedings of the 4th Symposium on Network Cloud Computing and Applications (NCCA), June 2015
Mastroianni, C., Meo, M., Papuzzo, G.: Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans. Cloud Comput. 1(2), 215–228 (2013)
Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2008)
Marotta, A., Avallone, S.: A simulated annealing based approach for power efficient virtual machines consolidation. In: Proceedings of 8th International Conference on Cloud Computing (CLOUD). IEEE (2015)
Drutskoy, D., Keller, E., Rexford, J.: Scalable network virtualization in software-defined networks. IEEE Internet Comput. 17(2), 20–27 (2013)
Shojafar, M., Canali, C., Lancellotti, R.: A computation- and network-aware energy optimization model for virtual machines allocation. In: Proceedings of International Conference on Cloud Computing and Services Science (CLOSER 2017), Porto, Portugal, April 2017
Akyildiz, I.F., Lee, A., Wang, P., Luo, M., Chou, W.: Research challenges for traffic engineering in software defined networks. IEEE Network 30(3), 52–58 (2016)
Eramo, V., Miucci, E., Ammar, M.: Study of reconfiguration cost and energy aware vne policies in cycle-stationary traffic scenarios. IEEE J. Sel. Areas Commun. 34(5), 1281–1297 (2016)
Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 243–264. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89856-6_13
Cao, Z., Dong, S.: An energy-aware heuristic framework for virtual machine consolidation in cloud computing. J. Supercomputing 69(1), 429–451 (2014)
Gu, L., Zeng, D., Guo, S., Ye, B.: Joint optimization of VM placement and request distribution for electricity cost cut in geo-distributed data centers. In: 2015 International Conference on Computing, Networking and Communications (ICNC), pp. 717–721. IEEE (2015)
Eramo, V., Cianfrani, A., Miucci, E., Listanti, M., Carletti, D., Gentilini, L.: Virtualization and virtual router migration: application and experimental validation. In: Proceedings of International Teletraffic Congress (ITC), pp. 1–6. IEEE (2014)
Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Cluster Comput. 18(1), 385–402 (2015)
Yi, Q., Singh, S.: Minimizing energy consumption of fattree data center networks. SIGMETRICS Perform. Eval. Rev. 42(3), 67–72 (2014)
Mandal, U., Habib, M.F., Zhang, S., Mukherjee, B., Tornatore, M.: Greening the cloud using renewable-energy-aware service migration. IEEE Netw. 27(6), 36–43 (2013)
Wood, T., Ramakrishnan, K., Hwang, J., Liu, G., Zhang, W.: Toward a software-based network: integrating software defined networking and network function virtualization. IEEE Netw. 29(3), 36–41 (2015)
Chiaraviglio, L., Ciullo, D., Mellia, M., Meo, M.: Modeling sleep mode gains in energy-aware networks. Comput. Netw. 57(15), 3051–3066 (2013)
Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A.: Live migration of virtual machines. In: Proceedings of 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2. USENIX Association (2005)
Canali, C., Lancellotti, R.: Scalable and automatic virtual machines placement based on behavioral similarities. Computing 99(6), 575–595 (2017)
Huang, D., Yang, D., Zhang, H., Wu, L.: Energy-aware virtual machine placement in data centers. In: Proceedings of Global Communications Conference (GLOBECOM). IEEE, Anaheim, December 2012
Acknowledgement
The authors acknowledge the support of the University of Modena and Reggio Emilia through the project \(S^2C\): Secure, Software-defined Clouds.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Canali, C., Lancellotti, R., Shojafar, M. (2018). An Optimization Model to Reduce Energy Consumption in Software-Defined Data Centers. In: Ferguson, D., Muñoz, V., Cardoso, J., Helfert, M., Pahl, C. (eds) Cloud Computing and Service Science. CLOSER 2017. Communications in Computer and Information Science, vol 864. Springer, Cham. https://doi.org/10.1007/978-3-319-94959-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-94959-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94958-1
Online ISBN: 978-3-319-94959-8
eBook Packages: Computer ScienceComputer Science (R0)