Abstract
Cloud computing is not only a popular paradigm for services offered over the Internet, but has also captured the interest of both academia and industry.
This is a preview of subscription content, log in via an institution.
References
Prasad, A. S., & Rao, S. (2014). A mechanism design approach to resource procurement in cloud computing. IEEE Transactions on Computers, 63(1), 17–30. https://doi.org/10.1109/TC.2013.106.
Prasad, G. V., Prasad, A. S., & Rao, S. (2017). A combinatorial auction mechanism for multiple resource procurement in cloud computing. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2016.2541150, supersedes https://doi.org/10.1109/10.1109/ISDA.2012.6416561.
S. Parsons, J. A. Rodriguez-Aguilar, & Klein, M. (2011, January). Auctions and bidding: A guide for computer scientists. ACM Computing Surveys, 43(2). https://doi.org/10.1145/1883612.1883617.
Nisan, N., & Ronen, A. (2001). Algorithmic mechanism design. Games and Economic Behavior, 35(1–2), 166–196.
Székely, G., & Rizzo, M. L. (2007). The uncertainty principle of game theory. The American Mathematical Monthly, 114, 688–702.
Pillai, P. S., & Rao, S. (2013). A resource allocation mechanism using coalition formation and the uncertainty principle of game theory. In 7th annual IEEE international systems conference (IEEE SysCon 2013), (pp. 178–184). Orlando, FL, April 2013. https://doi.org/10.1109/SysCon.2013.6549878.
Pillai, P. S., & Rao, S. (2016). Resource allocation in cloud computing using the uncertainty principle of game theory. The IEEE Systems Journal, 10(2), 637–648. https://doi.org/10.1109/JSYST.2014.2314861.
Narayan, A., Rao, S., Ranjan, G., & Dheenadayalan, K. (2012, March). Smart metering of cloud services. In 6th annual IEEE international systems conference (IEEE SysCon. 2012). BC, Canada: Vancouver. https://doi.org/10.1109/SysCon.2012.6189462.
Narayan, A., & Rao, S. (2014, September). Power-aware cloud metering. IEEE Transactions on Services Computing 440–451. https://doi.org/10.1109/TSC.2013.22.
Mas-Colell, A., Whinston, M. D., Green, J. R. (1995, June). Microeconomic theory. Oxford University Press.
Myerson, R. B. (1981). Optimal auction design. Mathematics of Operations Research, 6(1), 58–73.
Mithani, M. F., Salsburg, M., & Rao, S. (2010). A decision support system for moving workloads to public clouds. GSTF International Journal on Computing, 1(1), 150–157. https://doi.org/10.5176_2010-2283_1.1.25.
Bichler, M., Kalagnanam, J., Katircioglu, K., King, A. J., Lawrence, R. D., & Lee, H. S., et al. (2002). Applications of flexible pricing in business-to-business electronic commerce. IBM System Journal, 41(2), 287–302.
Narahari, Y., Raju, C., Ravikumar, K., & Shah, S. (2005). Dynamic pricing models for electronic business. Sadhana, 30, 231–256.
Iyengar, G., & Kumar, A. (2008). Optimal procurement mechanisms for divisible goods with capacitated suppliers. Review of Economic Design, 12(2), 129–154.
Buyya, R., Abramson, D., Giddy, J., & Stockinger, H. (2002). Economic models for resource management and scheduling in Grid computing. Concurrency and Computation: Practice and Experience, 14(13–15), 1507–1542.
Narahari, Y., Garg, D., Narayanam, R., & Prakash, H. (2009). Game theoretic problems in network economics and mechanism design solutions. Springer.
Li Mingbiao, L. J., & Shengli, X. (2007). Posted price model based on grs and its optimization using in grid resource allocation. In International conference on wireless communications, networking and mobile computing, 2007. WiCom 2007, September 2007 (pp. 3172–3175).
Subramoniam, K., Maheswaran, M., & Toulouse, M. (2000). Towards a micro-economic model for resource allocation in grid computing systems. In Canadian conference on electrical and computer engineering, 2002. IEEE CCECE 2002 (Vol. 2, 2002, pp. 782–785).
Parsa, S., Shokri, A., & Nourossana, S. (2009). A novel market based grid resource allocation algorithm. In First international conference on networked digital technologies, 2009. NDT ’09, July 2009 (pp. 146–152).
Lin, W.-Y., Lin, G.-Y., & Wei, H.-Y. (2010). Dynamic auction mechanism for cloud resource allocation. In CCGRID ’10: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (pp. 591–592). Washington, DC, USA: IEEE Computer Society.
Rochwerger, B., Tordsson, J., Ragusa, C., Breitgand, D., Clayman, S., & Epstein, A., et al. (2011, March). RESERVOIR—when one cloud is not enough. IEEE Computer.
Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer.
Saaty, T. (1980). The analytic hierarchy process, planning, piority setting, resource allocation. New York: McGraw-Hill.
Shoham, Y., & Leyton-Brown, K. (2008, December). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press.
Gibbard, A. (1973). Manipulation of voting schemes: A general result. Econometrica, 41(4), 587–601. https://doi.org/10.2307/1914083.
d’Aspremont, C., & Gérard-Varet, L.-A. (1979). Incentives and incomplete information. Journal of Public Economics, 11(1), 25–45.
Katzman, B., Reif, J., & Schwartz, J. A. (2010). The relation between variance and information rent in auctions. International Journal of Industrial Organization, 28(2), 127–130.
Vries, S. D., & Vohra, R. (2000). Combinatorial auctions: A survey, Northwestern University, Center for Mathematical Studies in Economics and Management Science, Discussion Papers, 2000. http://EconPapers.repec.org/RePEc:nwu:cmsems:1296
Sandholm, T., & Suri, S. (2000). Improved algorithms for optimal winner determination in combinatorial auctions and generalizations. In Proceedings of the seventeenth national conference on artificial intelligence and twelfth conference on innovative applications of artificial intelligence (pp. 90–97). AAAI Press.
Sandholm, T. (2002). Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence, 135(12), 1–54.
Goncalves, G. E., Endo, P. T., & Damasceno, T. (2011). Resource allocation in clouds: Concepts, tools and research challenges. In 29th Simpósio Brasileiro de Redes de Computadores.
Enumula, P. K. (2008, June). Coalition formation in multi-agent systems with uncertain task information. Master’s thesis, International Institute of Information Technology—Bangalore.
Heisenberg, W. (1927). Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift für Physik, 43(3–4), 172–198. https://doi.org/10.1007/BF01397280.
Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9–15.
Windows Azure. Retrieved June, 2013, from http://www.windowsazure.com.
Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., & Zagorodnov, D. (2009). The Eucalyptus open-source cloud-computing system. In Ninth IEEE/ACM international symposium on cluster computing and the grid (CCGrid 2009) (pp. 124–131). https://doi.org/10.1109/CCGRID.2009.93.
Ristenpart, T., Tromer, E., Shacham, H., & Savage, S. (2009). Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In Sixteenth ACM conference on computer and communications security (CCS ’09) (pp. 199–212). https://doi.org/10.1145/1653662.1653687.
Pritzker, P., & Gallagher, P. (2013, July). NIST cloud computing standards roadmap (pp. 500–291). NIST Special Publication.
Abood, D., Murdoch, R., N’Diay, S., Albano, D., Kofmehl, A., & Tung, T. (2010). Cloud computing and sustainability: The environmental benefits of moving to the cloud. Accenture in Collaboration with WSP Environment and Energy, Technical report. Retrieved from http://www.goo.gl/4QNigm.
Kansal, A., Zhao, F., Liu, J., Kothari, N., & Bhattacharya, A.A. (2010). Virtual machine power metering and provisioning. In Proceedings of the first ACM symposium on cloud computing, ser. SoCC ’10 (pp. 39–50). ACM. https://doi.org/10.1145/1807128.1807136.
Verma, A., Dasgupta, G., Nayak, T. K., De, P., & Kothari, R. (2009). Server workload analysis for power minimization using consolidation. In Proceedings of the 2009 USENIX annual technical conference, ser. USENIX’09 (p. 28). USENIX Association.
Uddin, M., & Rahman, A. A. (2010). Server consolidation: An approach to make data centers energy efficient and green. CoRR. arXiv:1010.5037.
Ganesan, R., Sarkar, S., & Narayan, A. (2012). Analysis of SAAS business platform workloads for sizing and collocation. In 2012 IEEE 5th international conference on cloud computing (CLOUD) (pp. 868–875). IEEE.
Bezemer, C.-P., Zaidman, A., Platzbeecker, B., Hurkmans, T., & Hart, A. (2010). Enabling multi-tenancy: An industrial experience report. In 2010 IEEE international conference on software maintenance (ICSM) pp. 1–8. IEEE.
Amazon EC2 pricing. Retrieved from http://www.goo.gl/ysnIAf.
Rackspace cloud servers: Pricing. Retrieved from http://www.goo.gl/6wMnvg.
CSC Cloud Usage Index (2011, December). CSC, Technical report. Retrieved from http://www.goo.gl/SAoUq4.
Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2013). Time series analysis: Forecasting and control. Wiley.
The R Project for Statistical Computing. Retrieved from http://www.r-project.org/.
Barroso, L. A. (2005). The price of performance. ACM Queue, 3(7), 48–53.
Curtis, P. M. (2007). Maintaining mission critical systems in a 24/7 environment. Wiley-IEEE Press. ISBN: 978-0471683742.
Brill, K. (2008, November). Understanding the true cost of operating a server. Facilitiesnet. Retrieved from http://www.facilitiesnet.com/datacenters/article/Understanding-the-True-Cost-of-Operating-a-Server--10063
Ranganathan, P., Leech, P., Irwin, D., & Chase, J. (2006). Ensemble-level power management for dense blade servers. SIGARCH Computer Architecture News, 34(2), 66–77. https://doi.org/10.1145/1150019.1136492.
Deng, Q., Meisner, D., Ramos, L., Wenisch, T. F., & Bianchini, R. (2011). Memscale: active low-power modes for main memory. SIGARCH Computer Architecture News, 39(1), 225–238. https://doi.org/10.1145/1961295.1950392.
Economou, D., Rivoire, S., & Kozyrakis, C. (2006). Full-system power analysis and modeling for server environments. In In workshop on modeling benchmarking and simulation (MOBS).
Krishnan, B., Amur, H., Gavrilovska, A., & Schwan, K. (2011). Vm power metering: Feasibility and challenges. SIGMETRICS Performance Evaluation Review, 38(3), 56–60. https://doi.org/10.1145/1925019.1925031.
Bircher, W., & John, L. (2012). Complete system power estimation using processor performance events. IEEE Transactions on Computers, 61(4), 563–577. https://doi.org/10.1109/TC.2011.47.
Heath, T., Diniz, B., Carrera, E. V., Meira, W., Jr., & Bianchini, R. (2005). Energy conservation in heterogeneous server clusters. In Proceedings of the tenth ACM SIGPLAN symposium on principles and practice of parallel programming, ser. PPoPP ’05 (pp. 186–195). ACM. https://doi.org/10.1145/1065944.1065969.
Chen, Q., Grosso, P., van der Veldt, K., de Laat, C., Hofman, R., & Bal, H. (2011, December). Profiling energy consumption of VMS for green cloud computing. In 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC) (pp. 768–775). https://doi.org/10.1109/DASC.2011.131.
Kivity, A., Kamay, Y., Laor, D., Lublin, U., & Liguori, A. (2007). KVM: The linux virtual machine monitor. Technical report. Retrieved from http://www.goo.gl/P20ueu.
McCullough, J. C., Agarwal, Y., Chandrashekar, J., Kuppuswamy, S., Snoeren, A. C., & Gupta, R. K. (2011). Evaluating the effectiveness of model-based power characterization. In Proceedings of the 2011 USENIX conference on USENIX annual technical conference, ser. USENIXATC’11 (pp. 12–12). USENIX Association.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Narayan, A., Pillai, P.S., Prasad, A.S., Rao, S. (2017). Resource Procurement, Allocation, Metering, and Pricing in Cloud Computing. In: Chaudhary, S., Somani, G., Buyya, R. (eds) Research Advances in Cloud Computing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5026-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-5026-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5025-1
Online ISBN: 978-981-10-5026-8
eBook Packages: Computer ScienceComputer Science (R0)