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Optimal Resource Rental Management

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Resource Management in Utility and Cloud Computing

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

Application services using cloud computing infrastructure are proliferating over the Internet. In this chapter, we study the problem of how to minimize resource rental cost associated with hosting such cloud-based application services, while meeting the projected service demand. This problem arises when applications incur significant storage and network transfer cost for data. Therefore, an Application Service Provider (ASP) needs to carefully evaluate various resource rental options before finalizing the application deployment. We choose Amazon®; EC2 marketplace as a case of study, and analyze the optimal strategy that exploits the tradeoff of data caching versus computing on demand for resource rental planning in cloud. Given fixed resource pricing, we first develop a deterministic model, using a mixed integer linear program, to facilitate resource rental decision making. Next, we investigate planning solutions to a resource market featuring time-varying pricing. We conduct time-series analysis over the spot price trace and examine its predictability using Auto-Regressive Integrated Moving-Average (ARIMA). We also develop a stochastic planning model based on multistage recourse. By comparing these two approaches, we discover that spot price forecasting does not provide our planning model with a crystal ball due to the weak correlation of past and future price, and the stochastic planning model better hedges against resource pricing uncertainty than resource rental planning using forecast prices.

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Notes

  1. 1.

    Amazon®; has declared lower pricing for EC2 when we prepared this manuscript. Since our simulation is based on [4], the study presented here is by no means up-to-date, but serves as a representative case of study.

  2. 2.

    Prices that are more than ± 10 % from the actual prices are out of the actual price range.

References

  1. Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., Tsafrir, D.: Deconstructing amazon ec2 spot instance pricing. In: IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom 2011), pp. 304–311 (2011)

    Google Scholar 

  2. AIMMS Optimization Software. Available: http://www.aimms.com/

  3. Andrzejak, A., Kondo, D., Yi, S.: Decision model for cloud computing under sla constraints. In: Proceedings of the 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS ’10), pp. 257–266 (2010)

    Google Scholar 

  4. Berriman, G.B., Deelman, E., Juve, G., Regelson, M., Plavchan, P.: The application of cloud computing to astronomy: A study of cost and performance. CoRR (2010)

    Google Scholar 

  5. Birge, J.R.: Decomposition and partitioning methods for multistage stochastic linear programs. Operations Research 33(5), 989–1007 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  6. Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day, Incorporated (1990)

    Google Scholar 

  7. Buyya, R., Ranjan, R., Calheiros, R.: Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services. In: Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science, vol. 6081, pp. 13–31 (2010)

    Google Scholar 

  8. Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: IEEE Asia-Pacific Services Computing Conference (APSCC ’09), pp. 103–110 (2009)

    Google Scholar 

  9. Chakaravarthy, V.T., Parija, G.R., Roy, S., Sabharwal, Y., Kumar, A.: Minimum cost resource allocation for meeting job requirements. In: 2011 IEEE International Parallel Distributed Processing Symposium (IPDPS ’11), pp. 14–23 (2011)

    Google Scholar 

  10. Chohan, N., Castillo, C., Spreitzer, M., Steinder, M., Tantawi, A., Krintz, C.: See spot run: using spot instances for mapreduce workflows. In: Proceedings of the 2nd USENIX conference on Hot topics in cloud computing (HotCloud’10), pp. 7–7 (2010)

    Google Scholar 

  11. Cloud Exchange. Http://www.cloudexchange.org/

  12. IBM ILOG CPLEX optimizer [online]. Available: http://www-01.ibm.com/software/integration/optimization/cplex-optimizer/

  13. Deelman, E., Singh, G., Livny, M., Berriman, B., Good, J.: The cost of doing science on the cloud: the montage example. In: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (SC ’08) (2008)

    Google Scholar 

  14. Demberel, A., Chase, J., Babu, S.: Reflective control for an elastic cloud application: an automated experiment workbench. In: Proceedings of the 2009 conference on Hot topics in cloud computing (HotCloud’09) (2009)

    Google Scholar 

  15. EC2 Spot Instance. http://aws.amazon.com/ec2/spot-instances/

  16. Market Trends: Platform as a Service, Worldwide, 2012–2016, 2H12 Update. ID: G00239236 (5 October 2012)

    Google Scholar 

  17. Gong, Z., Gu, X., Wilkes, J.: Press: Predictive elastic resource scaling for cloud systems. In: 2010 International Conference on Network and Service Management (CNSM ’10), pp. 9–16 (2010)

    Google Scholar 

  18. Jacob, J.C., Katz, D.S., Berriman, G.B., Good, J.C., Laity, A.C., Deelman, E., Kesselman, C., Singh, G., Su, M., Prince, T.A., Williams, R.: Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking. Int. J. Comput. Sci. Eng. 4, 73–87 (2009)

    Google Scholar 

  19. Mattess, M., Vecchiola, C., Buyya, R.: Managing peak loads by leasing cloud infrastructure services from a spot market. In: Proceedings of the 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC ’10), pp. 180–188 (2010)

    Google Scholar 

  20. Mazzucco, M., Dumas, M.: Achieving performance and availability guarantees with spot instances. In: Proceedings of the 13th International Conference on High Performance Computing and Communications (HPCC’11) (2011)

    Google Scholar 

  21. Mishra, A.K., Hellerstein, J.L., Cirne, W., Das, C.R.: Towards characterizing cloud backend workloads: insights from google compute clusters. SIGMETRICS Perform. Eval. Rev. 37(4), 34–41 (2010)

    Article  Google Scholar 

  22. Monti, H.M., Butt, A.R., Vazhkudai, S.S.: Catch: A cloud-based adaptive data transfer service for hpc. In: 2011 IEEE International Parallel Distributed Processing Symposium (IPDPS ’11), pp. 1242–1253 (2011)

    Google Scholar 

  23. Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. SIGOPS Oper. Syst. Rev. 41, 289–302 (2007)

    Article  Google Scholar 

  24. forecast package for R [online]. Available: http://robjhyndman.com/software/forecast/

  25. Song, Y., Zafer, M., Lee, K.W.: Optimal bidding in spot instance market. In: IEEE INFOCOM 2012, pp. 190–198 (2012)

    Google Scholar 

  26. SpotCloud. http://www.spotcloud.com/

  27. How to run MapReduce in Amazon EC2 spot market. Available: http://huanliu.wordpress.com/2011/06/22/how-to-run-mapreduce-in-amazon-ec2-spot-market/

  28. Urgaonkar, B., Chandra, A.: Dynamic provisioning of multi-tier internet applications. In: Proceedings of the Second International Conference on Automatic Computing (ICAC ’05), pp. 217–228 (2005)

    Google Scholar 

  29. Yuan, D., Yang, Y., Liu, X., Chen, J.: A cost-effective strategy for intermediate data storage in scientific cloud workflow systems. In: 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS ’10), pp. 1–12 (2010)

    Google Scholar 

  30. Zhang, J., Kim, J., Yousif, M., Carpenter, R., Figueiredo, R.J.: System-level performance phase characterization for on-demand resource provisioning. In: Proceedings of the 2007 IEEE International Conference on Cluster Computing (CLUSTER ’07), pp. 434–439 (2007)

    Google Scholar 

  31. Zhang, Q., Gürses, E., Boutaba, R., Xiao, J.: Dynamic resource allocation for spot markets in clouds. In: Proceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprise networks and services (Hot-ICE’11) (2011)

    Google Scholar 

  32. Zhao, H.: Exploring Cost-Effective Resource Management Strategies in the Age of Utility Computing. Ph.D. thesis, University of Florida, Gainesville, FL, USA (2013)

    Google Scholar 

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Authors and Affiliations

  1. University of Florida, Gainesville, FL, USA

    Han Zhao & Xiaolin Li

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  1. Han Zhao
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  2. Xiaolin Li
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Zhao, H., Li, X. (2013). Optimal Resource Rental Management. In: Resource Management in Utility and Cloud Computing. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8970-2_2

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  • DOI: https://doi.org/10.1007/978-1-4614-8970-2_2

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  • Online ISBN: 978-1-4614-8970-2

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