Abstract
Resource provisioning in Cloud providers is a challenge because of the high variability of load over time. On the one hand, the providers can serve most of the requests owning only a restricted amount of resources, but this forces to reject customers during peak hours. On the other hand, valley hours incur in under-utilization of the resources, which forces the providers to increase their prices to be profitable. Federation overcomes these limitations and allows providers to dynamically outsource resources to others in response to demand variations. Furthermore, it allows providers with underused resources to rent them to other providers. Both techniques make the provider getting more profit when used adequately. Federation of Cloud providers requires having a clear understanding of the consequences of each decision. In this paper, we present a characterization of providers operating in a federated Cloud which helps to choose the most convenient decision depending on the environment conditions. These include when to outsource to other providers, rent free resources to other providers (i.e., insourcing), or turn off unused nodes to save power. We characterize these decisions as a function of several parameters and implement a federated provider that uses this characterization to exploit federation. Finally, we evaluate the profitability of using these techniques using the data from a real provider.
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Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., et al. (2010). A view of Cloud computing. Communications of the ACM, 53(4), 50–58.
Barroso, L., & Hölzle, U. (2009). The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture, 4(1), 1–108.
Belady, C., Rawson, A., Pfleuger, D., & Cader, S. (2007). The green grid data center power efficiency metrics: PUE and DCiE. White paper, The Green Grid Consortium.
Boghosian, B., Coveney, P., Dong, S., Finn, L., Jha, S., Karniadakis, G., et al. (2007). NEKTAR, SPICE and vortonics: Using federated grids for large scale scientific applications. Cluster Computing, 10(3), 351–364.
Buyya, R., Yeo, C., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.
Campbell, R., Gupta, I., Heath, M., Ko, S., Kozuch, M., Kunze, M., et al. (2009). Open cirrusTM cloud computing testbed: Federated data centers for open source systems and services research. In USENIX workshop on hot topics in Cloud computing (HotCloud), 14–19 June 2009. San Diego, CA, USA.
Chu, X., Nadiminti, K., Jin, C., Venugopal, S., & Buyya, R. (2007). Aneka: Next-generation enterprise grid platform for e-Science and e-Business applications. In 3rd IEEE international conference on e-science and grid computing, 10–13 Dec 2007 (pp. 151–159). Bangalore, India.
Ejarque, J., de Palol, M., Goiri, I., Julia, F., Guitart, J., Badia, R., et al. (2008). SLA-driven semantically-enhanced dynamic resource allocator for virtualized service providers. In 4th IEEE international conference on e-science, 7–12 Dec 2008 (pp. 8–15). Indianapolis, USA.
Elnozahy, E., Kistler, M., & Rajamony, R. (2002). Energy-efficient server clusters. In 2nd workshop on power-aware computing systems, PACS 2002, 2 Feb 2002 (pp. 179–196). Cambridge, MA, U.S.A.: Springer.
Goiri, I., Fitó, J., Julià, F., Nou, R., Berral, J. L., Guitart, J., et al. (2010). Multifaceted resource management for dealing with heterogeneous workloads in virtualized data centers. In 11th ACM/IEEE international conference on grid computing (Grid 2010), 25–29 Oct 2010 (pp. 25–32). Brussels, Belgium.
Goiri, I., Guitart, J., & Torres, J. (2009). Elastic management of tasks in virtualized environments. In Proccedings of the XX jornadas de paralelismo (pp. 671–676).
Goiri, I., Julia, F., Fitó, J., Macías, M., & Guitart, J. (2010). Resource-level QoS metric for CPU-based guarantees in Cloud providers. In 7th international workshop on economics of grids, Clouds, systems, and services (GECON 2010) (pp. 34–47).
Heo, J., Henriksson, D., Liu, X., & Abdelzaher, T. (2007). Integrating adaptive components: An emerging challenge in performance-adaptive systems and a server farm case-study. In 28th IEEE international real-time systems symposium (RTSS 2007), 3–6 Dec 2007 (pp. 227–238). Tucson, AZ, USA.
Horvath, T., Abdelzaher, T., Skadron, K., & Liu, X. (2007). Dynamic voltage scaling in multi-tier web servers with end-to-end delay control. IEEE Transactions on Computers, 56(4), 444–458.
Jha, S., Merzky, A., & Fox, G. (2009). Using Clouds to provide grids with higher levels of abstraction and explicit support for usage modes. Concurrency and Computation: Practice and Experience, 21(8), 1087–1108.
Juan, A., Hernandez, F., Tordsson, J., Elmroth, E., Ali-Eldin, A., Zsigri, C., et al. (2011). OPTIMIS: A holistic approach to Cloud service provisioning. Future Generation Computer Systems. doi:10.1016/j.future.2011.05.022.
Jung, J. (2011). Service chain-based business alliance formation in service-oriented architecture. Expert Systems with Applications: An International Journal, 38(3), 2206–2211.
Koomey, J. (2007). Estimating total power consumption by servers in the U.S. and the world, 15 Feb 2007. Oakland, CA: Analytics Press.
Koomey, J. (2008). Worldwide electricity used in data centers. Environmental Research Letters, 3(3). doi:10.1088/1748-9326/3/3/034008.
Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., et al. (2009). The eucalyptus open-source Cloud-computing system. In 9th IEEE/ACM international symposium on cluster computing and the grid (CCGRID), 18–21 May 2009 (pp. 124–131). Shanghai, China.
Open Grid Forum (2011). Open Cloud Computing Interface (OCCI) Infrastructure, GFD-P-R.184, Version 1. Retrieved August 30, 2011 from http://www.ogf.org/documents/GFD.184.pdf.
Opitz, A., König, H., & Szamlewska, S. (2008). What does grid computing cost? Journal of Grid Computing, 6(4), 385–397.
Pinheiro, E., Bianchini, R., Carrera, E., & Heath, T. (2001). Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on compilers and operating systems for low power, 9 Sept 2001 (pp. 182–195). Barcelona, Spain.
Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., & Zhu, X. (2008). No power struggles: Coordinated multi-level power management for the data center. In 13th international conference on architectural support for programming languages and operating systems (ASPLOS 08), 1–5 Mar 2008. Seattle, WA, USA.
Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K., Llorente, I., et al. (2009). The RESERVOIR model and architecture for open federated Cloud computing. IBM Journal of Research & Development, 53(4), 535–545.
Sobolewski, M., & Kolonay, R. (2006). Federated grid computing with interactive service-oriented programing. Concurrent Engineering, 14(1), 55.
Turner, W., & Seader, J. (2006). Dollars per kW plus dollars per square foot are a better data center cost model than dollars per square foot alone. Uptime Institute white paper, Santa Fe.
Acknowledgements
This work is supported by the Ministry of Science and Technology of Spain and the European Union (FEDER funds) under contract TIN2007-60625 and grant AP2008-0264, by the Generalitat de Catalunya (2009-SGR-980).
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Goiri, Í., Guitart, J. & Torres, J. Economic model of a Cloud provider operating in a federated Cloud. Inf Syst Front 14, 827–843 (2012). https://doi.org/10.1007/s10796-011-9325-x
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DOI: https://doi.org/10.1007/s10796-011-9325-x