Abstract
As cloud services have increased in popularity, Information and Communication Technology (ICT) has been assuming a larger share of worldwide energy consumption. Energy cost resulting from ICT energy consumption contributes a significant part of the operating expenditure for cloud-service providers. We note that the energy cost at a geographical location has a direct correlation to the price of electricity (which may vary with time depending on the location). Different geographical regions of a country, e.g., the USA, may have different electricity prices with variation in prices from one time of the day to another. We show that this spatial and temporal variation in electricity price can be exploited to reduce the operating cost associated with energy consumption. Compute workloads can be relocated to regions with cheaper electricity prices. We consider a multi-datacenter (DC) environment where workloads are virtualized into virtual machines (VMs). In order to move VMs from one DC to another, we employ the live VM migration approach. Our model for cost-efficient VM migration is based on varying electricity prices such that it migrates VMs to DCs with cheaper electricity prices while simultaneously considering multiple parameters such as bandwidth for migration, cost of migration, duration of migration, and number of servers and racks to be switched on/off during each migration period. We find that our model significantly reduces the energy cost associated with cloud-service operation.
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Notes
Core routers such as a Cisco CRS-1 multi-shelf system can give a switching capacity of 92 Tb/s full duplex while consuming 1020 kW [21], so the power consumed in transmitting one bit is \(1.108\times 10^{-5}\) W.
References
ISO/RTO Council. http://www.isorto.org/about/default
Emerson Network Power. White Paper. http://www.emersonnetworkpower.com/documentation/white%20paper
Liu, H., Jin, H., Xu, C., Liao, X.: Performance and energy modeling for live migration of virtual machines. Cluster Comput. 16, 249–264 (2013)
Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for internet-scale systems. SIGCOMM 39, 123–134 (2009)
VMware Technical Paper. www.f5.com/pdf/white-papers/cloud-vmotion-f5-wp
Wood, T., Ramakrishnan, K., Van Der Merwe, J., Shenoy, P.: Cloudnet: A platform for optimized wan migration of virtual machines, University of Massachusetts Technical Report, TR-2010-002 (2010)
Mandal, U., Habib, M., Zhang, S., Tornatore, M., Mukherjee, B.: Greening the cloud using renewable-energy-aware service migration. IEEE Netw. 27, 36–43 (2013)
VMware and F5 Technical Brief. Enabling long distance live migration with f5 and vmware vmotion. https://f5.com/resources/white-papers/enabling-long-distance-live-migration-with-f5-and-vmware-vmotion
Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M., Pentikousis, K.: Energy-efficient cloud computing. Comput. J. 53, 1045–1051 (2010)
Baliga, J., Ayre, R., Hinton, K., Tucker, R.: Green cloud computing: balancing energy in processing, storage, and transport. Proc. IEEE 99, 149–167 (2011)
Zhang, Y., Chowdhury, P., Tornatore, M., Mukherjee, B.: Energy efficiency in telecom optical networks. IEEE Commun. Surv. Tutor. 12, 441–458 (2010)
Liu, Z., Lin, M., Wierman, A., Low, Andrew, S.L.: Greening geographical load balancing. In: Proceedings of ACM SIGMETRICS, pp. 233–244 (2011)
Gattulli, M., Tornatore, M., Fiandra, R., Pattavina, A.: Low-carbon routing algorithms for cloud computing services in IP-over-WDM networks. In: Proceedings of 2012 IEEE International Conference on Communications (ICC), pp. 2999–3003 (2012)
Chiaraviglio, L., Mellia, M., Neri, F.: Minimizing ISP network energy cost: formulation and solutions. IEEE/ACM Trans. Netw. 20, 463–476 (2012)
Schondienst, T., Davis, D., Plante, J., Vokkarane, V.: Renewable energy-aware manycast overlays, Technical Report, University of Massachusetts Lowell
Rao, L., Liu, X., Xie, L., Liu, W.: Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: Proceedings of IEEE INFOCOM (2010)
Le, K., Bianchini, R., Nguyen, T., Bilgir, O., Martonosi, M.: Capping the brown energy consumption of internet services at low cost. In: Proceedings of 2010 IEEE International Green Computing Conference, pp. 3–14 (2010)
Mandal, U., Habib, M., Zhang, S., Tornatore, M., Mukherjee, B.: Bandwidth and routing assignment for virtual machine migration in photonic cloud networks, In: ECOC 2013
Hewlett Packard. Technology Brief, Power efficiency and power management in HP proliant servers. http://h10032.www1.hp.com/ctg/manual/c03161908
Baliga, J., Hinton, K., Tucker, R.: Energy Consumption of the Internet. Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne (2011)
Gupta, A., Mandal, U., Chowdhury, P., Tornatore, M., Mukherjee, B.: Cost-efficient live VM migration based on varying electricity cost in optical cloud networks. In: IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (2014)
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This work was supported by NSF Grant No. CNS-1217978.
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Gupta, A., Mandal, U., Chowdhury, P. et al. Cost-efficient live VM migration based on varying electricity cost in optical cloud networks. Photon Netw Commun 30, 376–386 (2015). https://doi.org/10.1007/s11107-015-0555-6
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DOI: https://doi.org/10.1007/s11107-015-0555-6