Skip to main content
SpringerLink
Log in

Admission control in cloud computing using game theory

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cloud computing is emerging as a promising platform for ubiquitous computing where various types of resources are offered on pay-per-use basis. Cloud services are basically offered at three levels; infrastructure, platform and software. Service providers of these services are often interested to maximize their revenue and at the same time Cloud users expect for optimum quality of services. Sometimes, these two may conflict and admission of the requests is to be done that satisfies both Cloud providers and consumers. Game theory is a mathematical study of strategic decision making in which two players are involved in decision making based on their strategic moves. This work, applies the concept of game theory in admission control for Cloud requests. A model has been proposed and its performance study is done by simulating it in CloudSim simulator. Results are encouraging and may suggest for its possible inclusion in the Cloud middleware.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Fut Gen Comput Syst 29:1012–1023

    Article  Google Scholar 

  2. Li A, Yang X, Kandula S, Zhang M (2010) CloudCmp: comparing public cloud providers. In: Proceedings of the 10th annual conference on internet measurement, Melbourne, pp 1–14

  3. Baranwal G, Vidyarthi DP (2014) A framework for selection of best cloud service provider using ranked voting method. In: Proceedings of IEEE international advance computing conference (IACC), pp 831–837

  4. Pueschel T, Putzke F, Neumann D (2012) Revenue management for cloud providers-a policy-based approach under stochastic demand. In: Proceedings of 45th Hawaii international conference on system science, pp 1583–1592

  5. Wu L, Garg SK, Buyya R (2012) SLA-based admission control for a software-as-a-service provider in cloud computing environments. J Comput Syst Sci 78(5):1280–1299

    Article  Google Scholar 

  6. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Fut Gen Comput Syst 25(6):599–616

    Article  Google Scholar 

  7. Zhang Q, Zhu Q, Boutaba R (2011) Dynamic resource allocation for spot markets in cloud computing environments. In: Proceedings of 4th IEEE international conference on utility and cloud computing (UCC), pp 178-185

  8. Buyya R, Garg SK, Calheiros RN (2011) SLA-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. In: Proceedings of international conference on cloud and service computing (CSC), pp 1–10

  9. Mao M, Li J, Humphrey M (2010) Cloud auto-scaling with deadline and budget constraints. In: Proceedings of 11th IEEE/ACM international conference on grid computing (GRID), pp 41–48

  10. Moakar LA, Chrysanthis PK, Chung C, Guirguis S, Labrinidis A, Neophytou P, Pruhs K (2010) Admission control mechanisms for continuous queries in the cloud. In: Proceedings of IEEE 26th international conference on data engineering (ICDE), pp 409–412

  11. Feldman Z, Masin M, Tantawi AN, Arroyo D, Steinder M (2011) Using approximate dynamic programming to optimize admission control in cloud computing environment. In: Proceedings of the 2011 winter simulation conference, 2011, pp 3153–3164

  12. Konstanteli K, Varvarigou T, Cucinotta T (2011) Probabilistic admission control for elastic cloud computing. In: Proceedings of IEEE International conference on service-oriented computing and applications, pp 1–4

  13. Calheiros RN, Ranjan R, Buyya R (2011) Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In: Proceedings of IEEE international conference on parallel processing (ICPP), pp 295–304

  14. Yeo CS, Venugopal S, Chu X, Buyya R (2010) Autonomic metered pricing for a utility computing service. Fut Gen Comput Syst 26(8):1368–1380

    Article  Google Scholar 

  15. Jaideep DN, Varma MV (2010) Learning based Opportunistic admission control algorithms for map reduce as a service. In: Proceedings of the 3rd India software engineering conference (ISEC 2010), Mysore

  16. Rouskas AN, Kikilis AA, Ratsiatos SS (2006) Admission control and pricing in competitive wireless networks based on non-cooperative game theory. In: Proceedings of IEEE wireless communications and networking conference, vol 1, pp 205–210

  17. Lin H, Chatterjee M, Das SK, Basu K (2005) ARC: an integrated admission and rate control framework for competitive wireless CDMA data networks using noncooperative games. IEEE Trans Mob Comput 4(3):243–258

    Article  Google Scholar 

  18. Ye D, Chen J (2013) Non-cooperative games on multidimensional resource allocation. Fut Gen Comput Syst 29(6):1345–1352

    Article  Google Scholar 

  19. Pal R, Hui P (2013) Economic models for cloud service markets: pricing and capacity planning. Theor Comput Sci 496:113–124

    Article  MathSciNet  MATH  Google Scholar 

  20. Ardagna D, Panicucci B, Passacantando M (2011) A game theoretic formulation of the service provisioning problem in cloud systems. In: Proceedings of the 20th international conference on World wide web, pp 177–186

  21. Wei G, Vasilakos AV, Zheng Y, Xiong N (2010) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54(2):252–269

    Article  Google Scholar 

  22. Kim N, Jung Kyu-Hwan, Kim YS, Lee J (2012) Uniformly subsampled ensemble (USE) for churn management: theory and implementation. Expert Syst Appl 39:11839–11845

    Article  Google Scholar 

  23. Xie Y, Li X, Ngai EWT, Ying W (2009) Customer churn prediction using improved balanced random forests. Expert Syst Appl 36:5445–5449

    Article  Google Scholar 

  24. Tsai Chih-Fong, Lu Yu-Hsin (2009) Customer churn prediction by hybrid neural networks. Expert Syst Appl 36:12547–12553

    Article  MathSciNet  Google Scholar 

  25. Coussement K, Benoit DF, den Poel DV (2010) Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Syst Appl 37:2132–2143

    Article  Google Scholar 

  26. Gibbs MN, MacKay DJC (2000) Variational Gaussian process classifiers. IEEE Trans Neural Netw 11(6):1458–1464

    Article  Google Scholar 

  27. Bennani MN, Menasce DA (2005) Resource allocation for autonomic data centers using analytic performance models. In: Proceedings of the 2nd international conference on autonomic computing, pp 229–240

  28. Ai X, Marinescu DC, Boloni L, Siegel HJ, Daley RA, Wang I-Jeng (2008) A macroeconomic model for resource allocation in large-scale distributed systems. J Parallel Distrib Comput 68(2):182–199

    Article  Google Scholar 

  29. Paton NW, de Aragao MAT, Lee K, Fernandes AAA, Sakellariou R (2009) Optimizing utility in cloud computing through autonomic workload execution. IEEE Data Eng Bull 32:51–58

    Google Scholar 

  30. Roy N, Das SK, Basu K, Kumar M (2005) Enhancing availability of grid computational services to ubiquitous computing applications. In: Proceedings of 19th IEEE international parallel and distributed processing symposium, p 92

  31. Shoham Y, Leyton-Brown K (2009) Multiagent systems algorithmic, game-theoretic, and logical foundations, Cambridge University Press

  32. Basar T, Olsder GT (1999) Dynamic noncooperative game theory, 2nd edn. Soc. Industrial and Applied Math., Philadelphia

    MATH  Google Scholar 

  33. Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley, New York

    Google Scholar 

  34. Gustedt J, Jeannot E, Quinson M (2009) Experimental validation in large-scale systems: a survey of methodologies. Parallel Process Lett 19(3):399–418

    Article  MathSciNet  Google Scholar 

  35. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 1(41):23–50

  36. Garg SK, Buyya R (2013) An environment for modeling and simulation of message-passing parallel applications for cloud computing. Softw Pract Exp 43(11):1359–1375

  37. Kliazovich D, Bouvry P, Khan SU (2012) GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput 62(3):1263–1283

    Article  Google Scholar 

  38. McManus ML, Long MC, Copper A, Litavak E (2004) Queuing theory accurately models the need for critical care resources. Anesthesiology 100(5):1271–1276

    Article  Google Scholar 

  39. Wolff RW (1998) Poisson arrivals see time averages. Oper Res 30(2):223–231

    Article  Google Scholar 

  40. Amazon EC2 (2014). http://aws.amazon.com/

  41. Microsoft Azure (2014). http://azure.microsoft.com/en-us/

  42. RackSpace (2014). http://www.rackspace.com/

  43. GoGrid (2014). http://www.gogrid.com/

  44. Winer RS (2001) A framework for customer relationship management. Calif Manag Rev 43(4):89–105

    Article  Google Scholar 

  45. Valerio VD, Cardellini V, Presti FL (2013) Optimal pricing and service provisioning strategies in cloud systems: a Stackelberg game approach. In: Proceedings of 6th international conference on cloud computing, pp 115–122

  46. Anselmi J, Ardagna D, Passacantando M (2014) Generalized nash equilibria for saas/paas clouds. Eur J Oper Res 236(1):326–339

  47. Hassan MM, Hossain MS, Sarkar AMJ, Huh EN (2014) Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform. Inf Syst Front 16(4):523–542

    Article  Google Scholar 

  48. Jebalia M, Letaïfa AB, Hamdi M, Tabbane S (2013) A comparative study on game theoretic approaches for resource allocation in cloud computing architectures. In: Proceedings of 22nd International workshop on in enabling technologies: infrastructure for collaborative enterprises (WETICE), pp 336–341

Download references

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Gaurav Baranwal & Deo Prakash Vidyarthi

Authors
  1. Gaurav Baranwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deo Prakash Vidyarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deo Prakash Vidyarthi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baranwal, G., Vidyarthi, D.P. Admission control in cloud computing using game theory. J Supercomput 72, 317–346 (2016). https://doi.org/10.1007/s11227-015-1565-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-015-1565-y

Keywords

Search

Navigation