Abstract
Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption. Aligned with traditional commodity prices, PSVE is composed of two key elements: an individualized cost accounted from CPU usage of VMs (e.g., processing and networking), and a shared cost from common hypervisor management operations, proportionally distributed among VMs.
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Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distributed Comput. 6(5), 93–106 (2013)
Aldossary, M., Djemame, K.: Energy consumption-based pricing model for cloud computing, September 2016. This is an author produced version of a paper given at and published in 32nd UK Performance Engineering Workshop
Avelar, V., Azevedo, D., French, A.: Pue (tm): A Comprehensive Examination of the Metric. Technical report, The Green Grid (2012)
Barham, P, Dragovic, B, Fraser, K, Hand, S, Harris, T, Ho, A, Neugebauer, R, Pratt, I, Warfield, A: Xen and the art of virtualization. SIGOPS Oper. Syst. Rev. 37(5), 164–177 (2003). https://doi.org/10.1145/1165389.945462
Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer (2007)
Begum, S., Khan, M.K.: Potential of cloud computing architecture. In: 2011 International Conference on Information and Communication Technologies (ICICT), pp. 1–5. IEEE (2011)
Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831. IEEE Computer Society (2010)
Chawla, C., Chana, I.: Strategy-proof pricing approach for cloud market. arXiv:1506.06648 [cs] (2015)
Cheng, L., Rao, J., Lau, F.C.M.: vScale: automatic and efficient processor scaling for SMP virtual machines. In: Proceedings of the Eleventh European Conference on Computer Systems, EuroSys ’16, pp. 2:1–2:14, ACM, New York (2016)
Coroama, V., Hilty, L.M.: Energy consumed vs. energy saved by ICT - a closer look. In: 23rd Int. Conf. on Informatics for Environmental Protection (2009)
Cronkite-Ratcliff, B., Bergman, A., Vargaftik, S., Ravi, M., McKeown, N., Abraham, I., Keslassy, I.: Virtualized congestion control. In: Proceedings of the 2016 Conference on ACM SIGCOMM 2016 Conference, SIGCOMM ’16, pp. 230–243. ACM, New York (2016)
David, M.P., Schmidt, R.R.: Impact of ashrae environmental classes on data centers. In: Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2014 IEEE, 09 (2014)
Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: A Survey. IEEE Commun. Surv. Tutorials 18(1), 732–794 (2016)
ENP: Energy logic: Reducing data center energy consumption by creating savings that cascade across systems (2008)
Feller, E., Rohr, C., Margery, D., Morin, C.: Energy management in Iaas clouds: a holistic approach. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 204–212. IEEE (2012)
Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)
García García, A., Blanquer, I.: Cloud services representation using SLA composition. J. Grid Comput. 13(1), 35–51 (2015)
Gmach, D., Rolia, J., Cherkasova, L.: Resource and virtualization costs up in the cloud: models and design choices. In: Proc. 41St IEEE/IFIP DSN (2011)
Han, Y.: Cloud computing: case studies and total cost of ownership. Information Technology & Libraries (2011)
He, K., Rozner, E., Agarwal, K., Gu, Y. (Jason), Felter, W., Carter, J., Akella, A.: Ac/dc Tcp: virtual congestion control enforcement for datacenter networks. In: Proceedings of the 2016 Conference on ACM SIGCOMM 2016 conference, SIGCOMM ’16, pp. 244–257. New York, ACM . https://doi.org/10.1145/2934872.2934903 (2016)
Iyengar, M., Schmidt, R., Caricari, J.: Reducing energy usage in data centers through control of room air conditioning units. In: Proceedings of the IEEE ITherm Conference in Las Vegas, pp. 1–11, 07 (2010)
Kansal, N.J., Chana, I.: Energy-aware virtual machine migration for cloud computing - a firefly optimization approach. J. Grid Comput. 14(2), 327–345 (2016)
Kansal, S., Singh, G., Kumar, H., Kaushal, S.: Pricing models in cloud computing. In: Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, ICTCS ’14, pp. 33:1–33:5. ACM, New York (2014)
Kertesz, A., Dombi, J.D., Benyi, A.: A pliant-based virtual machine scheduling solution to improve the energy efficiency of iaas clouds. J. Grid Comput. 14(1), 41–53 (2016)
Koomey, J.G.: Worldwide electricity used in data centers. Environ. Res. Lett. 3(3), 034008 (2008)
Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)
Li, S., Lim, K., Faraboschi, P., Chang, J., Ranganathan, P., Jouppi, N.P.: System-level integrated server architectures for scale-out datacenters. In: Proc. 44th IEEE/ACM MICRO (2011)
Leong, B.G.L., Toombs, D.: Magic quadrant for cloud infrastructure as a service, worldwide. Gartner RAS Core Research (2015)
Mach, W., Schikuta, E.: Toward an economic and energy-aware cloud cost model. Concurrency Comput. Prac. Exp. 25(18), 2471–2487 (2013)
Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.-M., Vasilakos, A.V.: Cloud computing: Survey on energy efficiency. ACM Comput. Surv. 47(2), 33:1–33:36 (2014)
Meisner, D., Gold, B.T., Wenisch, Thomas F: Powernap: eliminating server idle power. In: ACM Sigplan Notices, vol. 44. ACM (2009)
Mell, P., Grance, T.: The NIST definition of cloud computing. In: Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology (2011)
Mobius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. In: IEEE Transactions on Parallel and Distributed Systems (2014)
Popek, G.J., Goldberg, R.P.: Formal requirements for virtualizable third generation architectures. Commun. ACM 17(7), 412–421 (1974). https://doi.org/10.1145/361011.361073
Reza Rahimi, M., Ren, J., Liu, C.H., Vasilakos, A.V., Venkatasubramanian, N.: Mobile cloud computing: a survey, state of art and future directions. Mobile Netw. Appl. 19(2), 133–143 (2014)
Rimal, B.P., Jukan, A., Katsaros, D., Goeleven, Y.: Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9(1), 3–26 (2011)
Ruck, D., Miers, C., Pillon, M., Koslovski, G.: Eavira: Energy-aware virtual infrastructure reallocation algorithm. In: 2017 VII Brazilian Symposium on Computing Systems Engineering, SBESC (2017)
Sharifi, L., Cerdà-Alabern, L., Freitag, F., Veiga, L.: Energy efficient cloud service provisioning keeping data center granularity in perspective. J. Grid Comput. 14(2), 299–325 (2016)
Shuja, J., Bilal, K., Madani, S.A., Khan, S.U.: Data center energy efficient resource scheduling. Clust. Comput. 17(4), 1265–1277 (2014). https://doi.org/10.1007/s10586-014-0365-0
Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016)
Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Proc. of the Conf. on Power Aware Computing and Systems. USENIX (2008)
Srinivasan, M.K., Sarukesi, K., Rodrigues, P., Sai Manoj, M., Revathy, P.: State-Of-The-Art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment. In: Proc. ICACCI’12, ACM (2012)
Stallings, W.: Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud. 1st edn. Addison-Wesley Professional, Boston (2015)
Tang, S., Lee, B.-S., He, B., Liu, H.: Long-term resource fairness: towards economic fairness on Pay-As-You-Use computing systems. In: Proc. of the 28Th ACM Int. Conf. on Supercomputing (2014)
Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-Optimal scheduling in hybrid Iaas clouds for deadline constrained workloads. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 228–235. IEEE (2010)
Verma, A., Kaushal, S.: Cost-time efficient scheduling plan for executing workflows in the cloud. J. Grid Comput. 13(4), 495–506 (2015)
Versick, D., Wassmann, I., Tavangarian, D.: Power consumption estimation of cpu and peripheral components in virtual machines. SIGAPP Appl. Comput. Rev. 13(3), 17–25 (2013). https://doi.org/10.1145/2537728.2537730
Primet, P.V.-B., Anhalt, F., Koslovski, G.: Exploring the virtual infrastructure service concept in grid’5000. In: 20th ITC Specialist Seminar on Network Virtualization. Hoi An, Vietnam (2009)
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
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The authors would like to thank LabP2D (http://labp2d.joinville.udesc.br) for providing the testbed resources and technical support, and the Santa Catarina State University (UDESC) research funding program.
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Hinz, M., Koslovski, G.P., Miers, C.C. et al. A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption. J Grid Computing 16, 493–512 (2018). https://doi.org/10.1007/s10723-018-9440-8
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DOI: https://doi.org/10.1007/s10723-018-9440-8