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An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

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Abstract

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.

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References

  1. Buyya R, Yeo CS, Venugopal S (2008) Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC’08), pp 5–13. doi:10.1109/HPCC.2008.172

  2. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616. doi:10.1016/j.future.2008.12.001

    Article  Google Scholar 

  3. Xia M, Koehler GJ, Whinston AB (2004) Pricing combinatorial auctions. Eur J Oper Res 154(1):251–270. doi:10.1016/s0377-2217(02)00678-1

    Article  MATH  MathSciNet  Google Scholar 

  4. Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurr Comput Pract Exp 14(13–15):1507–1542. doi:10.1002/cpe.690

    Article  MATH  Google Scholar 

  5. Amazon, “Amazon EC2 Spot Instances”. https://aws.amazon.com/ec2/spot/details/. Accessed 2 July 2016

  6. Zhang H, Jiang H, Li B, Liu F, Vasilakos AV, Liu J (2016) A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans Comput 65(3):805–818. doi:10.1109/infcom.2013.6566946

    Article  MATH  MathSciNet  Google Scholar 

  7. Luong NC, Wang P, Niyato D, Wen Y, Han Z (2017) Resource management in cloud networking using economic analysis and pricing models: a survey. IEEE Commun Surv Tutor. doi:10.1109/COMST.2017.2647981

  8. Parsons S, Rodriguez-Aguilar JA, Klein M (2011) Auctions and bidding: a guide for computer scientists. ACM Comput Surv (CSUR) 43(2):10. doi:10.1145/1883612.1883617

    Article  MATH  Google Scholar 

  9. McAfee RP, McMillan J (1987) Auctions and bidding. J Econ Lit 25(2):699–738

    MATH  Google Scholar 

  10. Shi W, Zhang L, Wu C, Li Z, Lau F (2014) An online auction framework for dynamic resource provisioning in cloud computing. ACM SIGMETRICS Perform Eval Rev 42(1):71–83. doi:10.1145/2637364.2591980

    Article  Google Scholar 

  11. Rubinstein A (1990) Game theory in economics. Edward Elgar, Cheltenham

    MATH  Google Scholar 

  12. Gibbons R (1992) Game theory for applied economists. Princeton University Press, Princeton

    Google Scholar 

  13. Zaman S, Grosu D (2012) An online mechanism for dynamic VM provisioning and allocation in clouds. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, pp 253–260. doi:10.1109/CLOUD.2012.26

  14. Lampe U, Siebenhaar M, Papageorgiou A, Schuller D, Steinmetz R (2012) Maximizing cloud provider profit from equilibrium price auctions. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, pp 83–90. doi:10.1109/CLOUD.2012.19

  15. Zaman S, Grosu D (2013) Combinatorial auction-based allocation of virtual machine instances in clouds. J Parallel Distrib Comput 73(4):495–508. doi:10.1016/j.jpdc.2012.12.006

    Article  Google Scholar 

  16. Lehmann D, Oćallaghan LI, Shoham Y (2002) Truth revelation in approximately efficient combinatorial auctions. J ACM 49(5):577–602. doi:10.1145/585265.585266

    Article  MATH  MathSciNet  Google Scholar 

  17. Zaman S, Grosu D (2013) A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds. IEEE Trans Cloud Comput 1(2):129–141. doi:10.1109/tcc.2013.9

    Article  Google Scholar 

  18. Zhang L, Li Z, Wu C (2014) Dynamic resource provisioning in cloud computing: a randomized auction approach. In: IEEE Conference on Computer Communications (INFOCOM 2014), pp 433–441. doi:10.1109/infocom.2014.6847966

  19. Prasad AS, Rao S (2014) A mechanism design approach to resource procurement in cloud computing. IEEE Trans Comput 63(1):17–30. doi:10.1109/tc.2013.106

    Article  MATH  MathSciNet  Google Scholar 

  20. Nejad MM, Mashayekhy L, Grosu D (2015) Truthful greedy mechanisms for dynamic virtual machine provisioning and allocation in clouds. IEEE Trans Parallel Distrib Syst 26(2):594–603. doi:10.1109/tpds.2014.2308224

    Article  Google Scholar 

  21. Zhang X, Huang Z, Wu C, Li Z, Lau F (2015) Online auctions in IaaS clouds: welfare and profit maximization with server costs. ACM SIGMETRICS Perform Eval Rev 43(1):3–15. doi:10.1145/2796314.2745855

    Article  Google Scholar 

  22. Mashayekhy L, Nejad MM, Grosu D (2015) A PTAS mechanism for provisioning and allocation of heterogeneous cloud resources. IEEE Trans Parallel Distrib Syst 26(9):2386–2399. doi:10.1109/TPDS.2014.2355228

    Article  Google Scholar 

  23. Kong W, Lei Y, Ma J (2016) Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism. Opt Int J Light Electron Opt 127(12):5099–5104. doi:10.1016/j.ijleo.2016.02.061

    Article  Google Scholar 

  24. Prasad GV, Prasad AS, Rao S (2016) A combinatorial auction mechanism for multiple resource procurement in cloud computing. IEEE Trans Cloud Comput. doi:10.1109/TCC.2016.2541150

  25. Mashayekhy L, Nejad MM, Grosu D, Vasilakos AV (2016) An online mechanism for resource allocation and pricing in clouds. IEEE Trans Comput 65(4):1172–1184. doi:10.1109/tc.2015.2444843

    Article  MATH  MathSciNet  Google Scholar 

  26. Chichin S, Vo QB, Kowalczyk R (2017) Towards efficient and truthful market mechanisms for double-sided cloud markets. IEEE Trans Serv Comput 10(1):37–51. doi:10.1109/TSC.2016.2594764

    Article  Google Scholar 

  27. Agmon Ben-Yehuda O, Ben-Yehuda M, Schuster A, Tsafrir D (2013) Deconstructing Amazon EC2 spot instance pricing. ACM Trans Econ Comput 1(3):16. doi:10.1145/2509413.2509416

    Article  Google Scholar 

  28. Javadi B, Thulasiramy RK, Buyya R (2011) Statistical modeling of spot instance prices in public cloud environments. In: Fourth IEEE International Conference on Utility and Cloud Computing (UCC 2011), pp 219–228. doi:10.1109/ucc.2011.37

  29. Zheng L, Joe-Wong C, Tan CW, Chiang M, Wang X (2015) How to bid the cloud. ACM SIGCOMM Comput Commun Rev 45(4):71–84. doi:10.1145/2829988.2787473

    Article  Google Scholar 

  30. Nadjaran Toosi A, Khodadadi F, Buyya R (2015) SipaaS: Spot instance pricing as a Service framework and its implementation in OpenStack. Concurr Comput Pract Exp 28(13):3672–3690. doi:10.1002/cpe.3749

    Article  Google Scholar 

  31. Teng F, Magoulès F (2010) A new game theoretical resource allocation algorithm for cloud computing. In: International Conference on Grid and Pervasive Computing. Springer, Berlin Heidelberg, pp 321–330. doi:10.1007/978-3-642-13067-0_35

  32. Nezarat A, Dastghaibyfard G (2016) A game theoretical model for profit maximization resource allocation in cloud environment with budget and deadline constraints. J Supercomput 72(12):4737–4770. doi:10.1007/s11227-016-1782-z

    Article  Google Scholar 

  33. Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard-real-time environment. J ACM 20(1):46–61. doi:10.1145/321738.321743

    Article  MATH  MathSciNet  Google Scholar 

  34. Li D, Chen C, Guan J, Zhang Y, Zhu J, Yu R (2016) DCloud: deadline-aware resource allocation for cloud computing jobs. IEEE Trans Parallel Distrib Syst 27(8):2248–2260. doi:10.1109/TPDS.2015.2489646

    Article  Google Scholar 

  35. Amazon, “Amazon EC2 On-Demand Pricing”. https://aws.amazon.com/ec2/pricing/on-demand/. Accessed 17 March 2017

  36. Microsoft, “Windows Azure Pricing”. https://azure.microsoft.com/en-us/pricing/. Accessed 17 March 2017

  37. “Parallel workloads archive”. http://www.cs.huji.ac.il/labs/parallel/workload/. Accessed 27 July 2016

  38. “Grid workloads archive”. http://gwa.ewi.tudelft.nl. Accessed 24 July 2016

  39. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50. doi:10.1002/spe.995

    Article  Google Scholar 

  40. Feitelson DG (2013) Parallel Workloads Archive: Standard Workload Format. http://www.cs.huji.ac.il/labs/parallel/workload/swf.html. Accessed 30 July 2016

  41. Feitelson DG (2015) Workload modeling for computer systems performance evaluation. Cambridge University Press, Cambridge. doi:10.1017/cbo9781139939690

  42. Iosup A, Li H, Dumitrescu C, Wolters L, Epema DH, “the Grid Workload Format”. http://gwa.ewi.tudelft.nl/fileadmin/pds/trace-archives/grid-workloads-archive/docs/TheGridWorkloadFormat_v001.pdf. Accessed 29 July 2016

  43. Iosup A, Li H, Jan M, Anoep S, Dumitrescu C, Wolters L, Epema DH (2008) The grid workloads archive. Future Gener Comput Syst 24(7):672–686. doi:10.1016/j.future.2008.02.003

    Article  Google Scholar 

  44. Amazon, “Amazon EC2 Instance Types”. https://aws.amazon.com/ec2/instance-types/. Accessed 1 July 2016

  45. Amazon, “Amazon EC2 Spot Instances Pricing”. https://aws.amazon.com/ec2/spot/pricing/. Accessed 2 July 2016

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Correspondence to Hossein Deldari.

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Salehan, A., Deldari, H. & Abrishami, S. An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds. J Supercomput 73, 4868–4905 (2017). https://doi.org/10.1007/s11227-017-2059-x

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