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Profit Maximization Analysis in Cloud Computing Using M/M/R/K Queuing System with Impatient Customers
|關鍵字:||服務水平協議;service level agreements;M/M/R/K排隊系統;系統阻擋率;中途退出率;M/M/R/K queuing system;blocking probability;reneging probability;loss probability.||出版社:||電機工程學系所||引用:|| The NIST Definition of Cloud Computing, http://www.nist.gov/itl/cloud/upload/cloud-def-v15.pdf.  M. Armbrust, A. Fox, R. Griffith, et al., “Above the clouds: a Berkeley view of cloud computing,” Technical Report No UCB/EECS-2009-28, University of California at Berkeley, USA, 2009.  H. Khazaei, J. Misic, and V. B. Misic, “Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 5, pages 936-943, 2012.  K. Wang, N. Li, and Z. Jiang, “Queueing System with Impatient Customers: A Review,” IEEE International Conference on Service Operations and Logistics and Informatics (SOLI), pages 82-87, 2010.  A. I. Pazgal, S. Radas, “Comparison of customer balking and reneging behavior to queueing theory predictions: An experimental study,” Computers & Operations Research, Volume 35, Issue 8, pages 2537-2548, 2008.  A. P. Ghosh, A. P. Weerasinghe, “Optimal buffer size and dynamic rate control for a queueing system with impatient customers in heavy traffic,” Stochastic Processes and their Applications, Volume 120, Issue 11, pages 2103-2141, 2010.  D. Doran, L. Lipsky and S. Thompson, “Cost-based Optimization of Buffer Size in M/G/1/N Systems Under Different Service-time Distributions,” Proceedings of 9th IEEE Network Computing and Applications (NCA), pages 28-35, 2010.  Y. C. Lee, C. Wang, A. Y. Zomaya, and B. B. Zhou, “Profit-driven scheduling for cloud services with data access awareness,” Journal of Parallel and Distributed Computing, vol. 72, pages 591-601, 2012.  B. Yang, F. Tan, Y. Dai, and S. Guo, “Performance Evaluation of Cloud Service Considering Fault Recovery,” Proc. First Int’l Conf. Cloud Computing (CloudCom ’09), pages 571-576, 2009.  Introduction to Data Center Infrastructure Management, http://www.raritan.com/resources/white-papers/dcim/Introduction-to-Data-Center-Infrastructure-Management.pdf, 2010.  Best Practices Guide for Energy-Efficient Data Center Design, http://www1.eere.energy.gov/femp/pdfs/eedatacenterbestpractices.pdf, 2011.  D. Gross, J. F. Shortle, J. M. Thompson and C. M. Harris. Fundamentals of Queuing Theory (Fourth edition), A John Wiley & Sons, Inc., New York, 2008.  P. Afeche and H. Mendelson, “Pricing and Priority Auctions in Queueing Systems with a Generalized Delay Cost Structure,” MANAGEMENT SCIENCE, Vol. 50, No. 7, pages 869–882, 2004.  P. Guo, P. Zipkin, “Analysis and Comparison of Queues with Different Levels of Delay Information,” MANAGEMENT SCIENCE Vol. 53, No. 6, pages 962–970, 2007.  J. Ou, B.M. Rao, “Benefits of providing amenities to impatient waiting customers,” Computers & Operations Research, Vol. 30, Issue 14, pages 2211-2225, 2003.  D. Durkee, “Why cloud computing will never be free,” Communications of the ACM, vol. 53, no. 5, pages 62-69, 2010.  A. P. Chandrakasan, S. Sheng, and R. W. Brodersen, “Low power CMOS digital design,” IEEE Journal on Solid-State Circuits, vol. 27, no. 4, pages 473-484, 1992.  B. Zhai, D. Blaauw, D. Sylvester, and K. Flautner, “Theoretical and practical limits of dynamic voltage scaling,” Proceedings of the 41st Design Automation Conference, pages 868-873, 2004.  CPU power dissipation, http://en.wikipedia.org/wiki/CPU_power_dissipation.  Central processing unit, http://en.wikipedia.org/wiki/Central_processing_unit.  CMOS, http://en.wikipedia.org/wiki/CMOS.  J. Cao, K. Hwang, L. Li, and A. Y. Zomaya, “Optimal multiserver configuration for profit maximization in cloud computing,” IEEE Transactions on Parallel and Distributed Systems, Vol. 24 , Issue: 6, pages 1087 - 1096, 2013.  J. Baliga, R.W. A. Ayre, K. Hinton and R. S. Tucker, “Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport,” Proceedings of The IEEE, vol. 99, no. 1, pages 149-167, 2011.  Y. C. Lee ‧ A. Y. Zomaya, “Energy efficient utilization of resources in cloud computing systems,” Journal of Supercomputing, 2012.||摘要:||
Cloud computing realize utility computing paradigm by offering shared resources through Internet-based. However, how resources provisioning can reduce the burden of operational cost and simultaneously meet service level agreements (SLAs) have become the main challenge for cloud providers. We model a cloud server farm with finite buffer as an M/M/R/K queuing system. Since no customers want to endure long waiting time, impatient customers are considered in our designed service system. In order to properly control system blocking rates and avoid excessive reneging rate, SLA is specified by guaranteeing: loss probability ≤ x, where x is a maximum threshold value. Three important issues are solved in this paper. First, tradeoff is conducted to resolve the conflicts between system performances and cost savings. Second, a profit function which includes system congestion costs and operational costs per unit time is developed. Blocking loss and reneging loss are also considered in revenue estimation. Satisfying SLA has the highest priority, followed by profit maximization. Finally, simulation results show that the optimal resources provisioning of service rate and buffer space can be obtained to maximize profit.
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