The Journal of Supercomputing

, Volume 72, Issue 12, pp 4737–4770 | Cite as

A game theoretical model for profit maximization resource allocation in cloud environment with budget and deadline constraints

  • Amin Nezarat
  • Gh. Dastghaibyfard


One significant challenge for the resource allocation in cloud environments is the pricing issue and selection of applicants of cloud resources on the basis of cloud economic parameters. Taking into account the fact that the resource allocation in cloud environments is an economic supply- and demand-based problem, economics-based methods result in better solutions in a shorter period of time. In this paper, using Bayesian method, where each user estimates other rivals’ actions in the next step of the auction, a game model for winner determination is proposed. a non-cooperative game theory mechanism based on combinatorial auction in an environment with incomplete information has been proposed to reach Nash equilibrium point and select the winners. Using the proposed method, an improvement of 17 % profit was obtained for the cloud provider and a 12 % boost was seen in the sold resources. The objective function suggested for bidding converged to the solution in all cases and was stable. In the following, it was proved that the proposed model has the possibility of attaining the best local bid.


Combinatorial auction Cloud resource allocation Nash equilibrium Non-cooperative game 


  1. 1.
    Armbrust M, Fox A, Griffith R (2009) Above the clouds: a berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley, Feb 2009Google Scholar
  2. 2.
    Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurr Comput Pract Exp 14:1507–1542CrossRefzbMATHGoogle Scholar
  3. 3.
    Fan Q, Wu Q, Magoul‘es F, Xiong N, Vasilakos AV, He Y (2009) Game and balance multicast architecture algorithms for sensor grid. Sensors 9(9):7177–7202CrossRefGoogle Scholar
  4. 4.
    Gibbons R (1992) A primer in game theory. Pearson Higher EducationGoogle Scholar
  5. 5.
    Sandholm TW (1999) Distributed rational decision making. Multiagent Syst Modern Approach Distrib Artif Intell 37:201–258Google Scholar
  6. 6.
    Wei G, Vasilakos A, Zheng Y, Xiong N (2009) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54:1–18Google Scholar
  7. 7.
    Kwong Kwok Y, Song S, Hwang K (2005) Selfish grid computing: Game-theoretic modeling and nash performance results. In: Proceedings of international symposium on cluster computing and the grid, pp 9–12Google Scholar
  8. 8.
    Galstyan A, Kolar S, Lerman K (2003) Resource allocation games with changing resource ca-pacities. In: Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems. ACM Press, pp 145–152Google Scholar
  9. 9.
    Bredin J, Kotz D, Rus D, Maheswaran RT, Imer C, Basar T (2003) Computational markets toregulate mobile-agent systems. Auton Agents Multi-Agent Syst 6:235–263CrossRefGoogle Scholar
  10. 10.
    Maheswaran RT, Basar T (2003) Nash equilibrium and decentralized negotiation in auctioning divisible resources. Group Decision Negot 12:361–395CrossRefGoogle Scholar
  11. 11.
    Khan S, Ahmad I (2006) Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation. In: Parallel and distributed processing symposium 101Google Scholar
  12. 12.
    An B, Miao C, Shen Z (2007) Market based resource allocation with incomplete information. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence. Morgan Kaufmann Publishers Inc, pp 1193–1198Google Scholar
  13. 13.
    Teng F, Magoul‘ es F (2010) A new game theoretical resource allocation algorithm for cloud computing. In: Proceedings of advances in grid and pervasive computing, vol 6104. Springer, New York, pp 321–330Google Scholar
  14. 14.
    Garg SK, Buyya R (2011) Market-orient ed resource management and scheduling: a taxonomy and survey. In: Obaidat MS, Misra S (eds) Cooperative networking. Wiley, Chichester, pp 277–306CrossRefGoogle Scholar
  15. 15.
    Sutherland IE (1968) A futures market in computer time. Commun ACM 11(6):449–451CrossRefGoogle Scholar
  16. 16.
    Gagliano RA, Fraser MD, Schaefer ME (1995) Auction allocation of computing resources. Commun ACM 38(6):88–102CrossRefGoogle Scholar
  17. 17.
    Gibney MA, Jennings NR, Vriend NJ, Griffiths J-M (1999) Market-based call routing in telecommunications networks using adaptive pricing and real bidding. In: Proceedings of the third international workshop on intelligent agents for telecommunication applications. Springer, New York, pp 46–61Google Scholar
  18. 18.
    Joita L, Rana OF, Freitag F, Chao I, Chacin P, Navarro L, Ardaiz O (2007) A catallactic market for data mining services. Future Gener Computer Syst 23(1):146–153CrossRefGoogle Scholar
  19. 19.
    Chun BN, Culler DE (2002) User-centric performance analysis of market-based cluster batch schedulers. In: Proceedings of the 2nd IEEE/ACM international symposium on cluster computing and the grid, Washington, DC, USA. IEEE Computer Society, p 30Google Scholar
  20. 20.
    Nguyen T-M-H, Magoul‘es F (2009) Autonomic data management system in grid environment. J Algorithms Comput Technol 3:155–177CrossRefGoogle Scholar
  21. 21.
    Stuer G, Vanmechelen K, Broeckhove J (2007) A commodity market algorithm for pricing substitutable grid resources. Future Gener Computer Syst 23(5):688–701CrossRefGoogle Scholar
  22. 22.
    Stratford N, Mortier R (1999) An economic approach to adaptive resource management. In: Proceedings of the 7th workshop on hot topics in operating systems. IEEE Computer Society, pp 142–147Google Scholar
  23. 23.
    Ozturan C (2004) Resource bartering in data grids. Sci Computer Program 12(3):155–168Google Scholar
  24. 24.
    Wolski R, Plank JS, Brevik J, Bryan T (2001) Analyzing market-based resource allocation strategies for the computational grid. Int J High Perform Comp Appl 15(3):258–281CrossRefGoogle Scholar
  25. 25.
    Gomoluch J, Schroeder M (2003) Market-based resource allocation for grid computing: a model and simulation. In: Proceedings 1st international workshop on middleware for grid computing, pp 211–218Google Scholar
  26. 26.
    Garg SK, Venugopal S, Broberg J, Buyya R (2013) Double auction-inspired meta-scheduling of parallel applications on global grids. J Parallel Distrib Comput (in press)Google Scholar
  27. 27.
    Das A, Grosu D (2005) Combinatorial auction-based protocols for resource allocation in grids. In: Proceedings of 19th international parallel and distributed processing symposium, 6th workshop on parallel and distributed scientific and engineering computingGoogle Scholar
  28. 28.
    Dash RK, Vytelingum P, Rogers A, David E, Jennings NR (2007) Market-based task allocation mechanisms for limited-capacity suppliers. IEEE Trans Syst Man Cybern Part A Syst Hum 37(3):391–405CrossRefGoogle Scholar
  29. 29.
    Grosu D (2004) AGORA: an architecture for strategyproof computing in grids. In: Proceedings of 3rd international symposium on parallel and distributed computing, pp 217–224Google Scholar
  30. 30.
    Wang H, Jing Q, Chen R, He B, Qian Z, Zhou L (2010) Distributed systems meet economics: pricing in the cloud. In: Proceedings of 2nd USENIX workshop on hot topics in cloud computingGoogle Scholar
  31. 31.
    Walker E, Brisken W, Romney J (2010) To lease or not to lease from storage clouds. IEEE Computer 43(4):44–50CrossRefGoogle Scholar
  32. 32.
    Li A, Yang X, Kandula S, Zhang M (2010) CloudCmp: shopping for a cloud made easy. In: Proceedings of 2nd USENIX workshop on hot topics in cloud computingGoogle Scholar
  33. 33.
    Buyya R, Ranjan R, Calheiros RN (2010) InterCloud: utility-oriented federation of cloud computing environments for scaling of application services. In: Proceedings of 10th International Conference on Algorithms and Architectures for Parallel Processing, pp 13–31Google Scholar
  34. 34.
    Altmann J, Courcoubetis C, Stamoulis GD, Dramitinos M, Rayna T, Risch M, Bannink C (2008) GridEcon: a market place for computing resources. In: Proceedings of workshop on grid economics and business models, pp 185–196Google Scholar
  35. 35.
    Risch M, Altmann J, Guo L, Fleming A, Courcoubetis C (2009) The GridEcon platform: a business scenario testbed for commercial cloud services. In: Proceedings of workshop on grid economics and business models, pp 46–59Google Scholar
  36. 36.
    Lin WY, Lin GY, Wei HY (2010) Dynamic auction mechanism for cloud resource allocation. In: Proceedings of 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp 591–592Google Scholar
  37. 37.
    Chohan N, Castillo C, Spreitzer M, Steinder M, Tantawi A, Krintz C (2010) See spot run: using spot instances for mapreduce workflows. In: Proceedings of 2nd USENIX workshop on hot topics in cloud computingGoogle Scholar
  38. 38.
    Campos-Nanez E, Fabra N, Garcia A (2007) Dynamic auctions for on-demand services. IEEE Trans Syst Man Cybern Part A Syst Hum 37(6):878–886CrossRefGoogle Scholar
  39. 39.
    Quiroz A, Kim H, Parashar M, Gnanasambandam N, Sharma N (2009) Towards autonomic workload provisioning for enterprise grids and clouds. In: Proceedings of 10th IEEE/ACM International Conference on Grid Computing, pp 50–57Google Scholar
  40. 40.
    Vecchiola C, Calheiros RN, Karunamoorthy D, Buyya R (2012) Deadline-driven provisioning of resources for scientific applications in hybrid clouds with aneka. Future Gener Computer Syst 28:58–65CrossRefGoogle Scholar
  41. 41.
    Chen W, Qiao X, Wei J, Huang T (2012) A profit-aware virtual machine deployment optimization framework for cloud platform providers. In: Proceedings of 5th IEEE International Conference on Cloud Computing, pp 17–24Google Scholar
  42. 42.
    Ghosh R, Naik VK (2012) Biting off safely more than you can chew: predictive analytics for resource over-commit in IaaS cloud. In: Proceedings of 5th IEEE International Conference on Cloud Computing, pp 25–32DGoogle Scholar
  43. 43.
    Lynar TM, Herbert RD, Simon S (2009) Auction resource allocation mechanisms in grids of heterogeneous computers. WSEAS Trans Computers 8(10):1671–1680Google Scholar
  44. 44.
    Wolski R, Plank JS, Brevik J, Bryan T (2001) G-commerce: market formulations controlling resource allocation on the computational grid. In: Proceedings of international parallel and distributed processing symposium, p 46Google Scholar
  45. 45.
    Wei G, Vasilakos A, Zheng Y, Xiong N (2009) A game-theoretic method of fair resource allocation forcloud computing services. J Supercomput 54:1–18Google Scholar
  46. 46.
    Sequeira Sh R, Karthikeyan P (2013) A survey on auction based resource allocation in cloud computing. Int J Res Computer Appl Robot 1(9):96–102Google Scholar
  47. 47.
    Anselmi J, Ardagna D, Passacantando M (2013) Generalized Nash equilibria for SaaS/PaaS clouds. Eur J Oper Res. doi: 10.1016/j.ejor.2013.12.007
  48. 48.
    Ardagna D, Panicucci B, Passacantando M (2013) Generalized Nash equilibria for the service provisioning problem in cloud systems. IEEE Trans Serv Comput 6(4):429–442CrossRefGoogle Scholar
  49. 49.
    Khanna G, Beaty K, Kar G, Kochut A (2006) Application performance management in virtualized server environments. In: Proceedings of the IEEE Network Operations and Management Symposium, pp 373–381Google Scholar
  50. 50.
    Steinder M, Whalley I, Carrera D, Gaweda I, Chess D (2007) Server virtualization in autonomic management of heterogeneous workloads. In Proceedings of the IEEE symposium on integrated network management, pp 139–148Google Scholar
  51. 51.
    Kephart J, Chan H, Levine D, Tesauro G, Rawson F, Le-furgy C (2007) Coordinating multiple autonomic managers to achieve specified power-performance tradeoffs. In: Proceedings of IEEE International Conference on Autonomic Computing (ICAC), pp 145–154Google Scholar
  52. 52.
    Ranganathan P, Leech P, Irwin D, Chase J (2006) Ensemble-level power management for dense blade servers. In: Proceedings of the IEEE symposium on computer architecture, pp 66–77Google Scholar
  53. 53.
    Pinheiro E, Bianchini R, Heath T (2003) Dynamic cluster reconfiguration for power and performance. Kluwer Academic Publishers, BerlinCrossRefzbMATHGoogle Scholar
  54. 54.
    Abdelzaher T, Shin KG, Bhatti N (2002) Performance guarantees for web server end-systems: a control-theoretical approach. IEEE Trans Parallel Distrib Syst 13(1):80–96CrossRefGoogle Scholar
  55. 55.
    Kusic D, Kandasamy N (2006) Risk-aware limited lookahead control for dynamic resource provisioning in enterprise computing systems. In: Proceedings of International Conference on Autonomic ComputingGoogle Scholar
  56. 56.
    Kusic D, Kephart JO, Kandasamy N, Jiang G (2008) Power and performance management of virtualized computing environments via lookahead control. In: Proceedings of International Conference on Autonomic ComputingGoogle Scholar
  57. 57.
    Qin W, Wang Q (2007) Modeling and control design for performance management of web servers via an LPV approach. IEEE Trans Control Syst Technol 15(2):259–275CrossRefGoogle Scholar
  58. 58.
    Raghavendra R, Ranganathan P, Talwar V, Wang Z, Zhu X (2013) No ‘Power’ struggles: coordinated multi-level power management for the data center. SIGARCH Computer Archit News 36(1):48–59CrossRefGoogle Scholar
  59. 59.
    Feitelson DG Parallel Workloads Archives: Logs.
  60. 60.
    Feitelson DG Parallel Workloads Archives: Standard Workload Format.
  61. 61.
    Zaman S, Grosu D (2013) A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds. IEEE Trans Cloud Comput 1(2):129–141CrossRefGoogle Scholar
  62. 62.
    Nezarat A, Dastghaibifard G (2015) Efficient nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction. PLoS One 10(10):e0138424. doi: 10.1371/journal.pone.0138424 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, IT College of Electrical and Computer EngineeringShiraz UniversityShirazIslamic Republic of Iran

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