Advertisement

A Procurement Market to Allocate Cloud Providers’ Residual Computing Capacity

  • Paolo Bonacquisto
  • Giuseppe Di Modica
  • Giuseppe Petralia
  • Orazio Tomarchio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8745)

Abstract

Commercial cloud providers are used to allocate computing resources to requesting customers according to the well known direct-sell, fixed-price mechanism. This mechanism is proved to be economically inefficient, as it does not account for the market’s supply-demand rate. Nevertheless, providers will unlikely abandon a pricing mechanism which is very easy and cheap to implement in favour of alternative schemes. On the other end, none of the commercial providers adopting the fixed-price mechanism is able to allocate their overall computing capacity. Not selling a single virtual machine within a predefined time slot means a profit loss to the provider. Alternative mechanisms are therefore needed to sell what we call the “residual” computing capacity, i.e., the capacity which the provider is not able to allocate through direct-sell. We argue that auction-based sells may meet this need. In this paper the design of a procurement market for computing resources is proposed. Also, an adaptive bidding strategy has been devised to help providers to maximize the revenue in the context of procurement auctions. Simulations have been run to test the responsiveness of the strategy to the provider’s business objective.

Keywords

Cloud Computing Virtual Machine Cloud Provider Computing Capacity Combinatorial Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., Tsafrir, D.: Deconstructing amazon ec2 spot instance pricing. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), pp. 304–311 (2011)Google Scholar
  2. 2.
    Bonacquisto, P., Di Modica, G., Petralia, G., Tomarchio, O.: Procurement auctions to maximize players’ utility in cloud markets. In: Proceedings of the 4th International Conference on Cloud Computing and Services Science, CLOSER 2014, Barcelona, Spain (April 2014)Google Scholar
  3. 3.
    Buyya, R., Yeo, C.S., Venugopal, S.: 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 2008), pp. 5–13 (September 2008)Google Scholar
  4. 4.
    Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. In: Software: Practice and Experience (2011)Google Scholar
  5. 5.
    Chard, K., Bubendorfer, K.: High Performance Resource Allocation Strategies for Computational Economies. IEEE Trans. Parallel Distrib. Syst. 24(1), 72–84 (2013)CrossRefGoogle Scholar
  6. 6.
    Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial auctions. The MIT Press (2005)Google Scholar
  7. 7.
    Di Modica, G., Petralia, G., Tomarchio, O.: Procurement auctions to trade computing capacity in the Cloud. In: 8th Int. Conf. on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2013), Compiegne, France (October 2013)Google Scholar
  8. 8.
    Di Modica, G., Tomarchio, O.: Matching the business perspectives of providers and customers in future cloud markets. Cluster Computing, 1–19 (2014)Google Scholar
  9. 9.
    Google: Traces of google workloads (2011), http://code.google.com/p/googleclusterdata/
  10. 10.
    Klemperer, P.: Auction Theory: A Guide to the Literature. Journal of Economic Surveys 13(3) (1999)Google Scholar
  11. 11.
    McAfee, R.P., McMillan, J.: Auctions and bidding. Journal of Economic Literature 15, 699–738 (1987)zbMATHGoogle Scholar
  12. 12.
    Parsons, S., Rodriguez-Aguilar, J.A., Klein, M.: Auctions and bidding: A guide for computer scientists. ACM Computing Surveys 43(2) (February 2011)Google Scholar
  13. 13.
    Risch, M., Altmann, J., Guo, L., Fleming, A., Courcoubetis, C.: The GridEcon Platform: A Business Scenario Testbed for Commercial Cloud Services. In: Altmann, J., Buyya, R., Rana, O.F. (eds.) GECON 2009. LNCS, vol. 5745, pp. 46–59. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. Information Sciences (in press, 2014)Google Scholar
  15. 15.
    Smeltzer, L.R., Carr, A.: Reverse auctions in industrial marketing and buying. Business Horizons 45(2), 47–52 (2002)CrossRefGoogle Scholar
  16. 16.
    Sulistio, A., Kim, K.H., Buyya, R.: Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue. In: 8th IEEE International Symposium on Cluster Computing and the Grid (CCGRID 2008), pp. 267–276 (May 2008)Google Scholar
  17. 17.
    Vinu Prasad, G., Rao, S., Prasad, A.: A Combinatorial Auction mechanism for multiple resource procurement in cloud computing. In: 2012 12th International Conference on Intelligent Systems Design and Applications, ISDA (2012)Google Scholar
  18. 18.
    Wang, Q., Ren, K., Meng, X.: When cloud meets ebay: Towards effective pricing for cloud computing. In: 2012 Proceedings IEEE INFOCOM, pp. 936–944 (2012)Google Scholar

Copyright information

© International Federation for Information Processing 2014

Authors and Affiliations

  • Paolo Bonacquisto
    • 1
  • Giuseppe Di Modica
    • 1
  • Giuseppe Petralia
    • 1
  • Orazio Tomarchio
    • 1
  1. 1.Department of Electrical, Electronic and Computer EngineeringUniversity of CataniaCataniaItaly

Personalised recommendations