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Optimal Hiring of Cloud Servers

  • Andrew Stephen McGough
  • Isi Mitrani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8721)

Abstract

A host uses servers hired from a Cloud in order to offer certain services to paying customers. It must decide dynamically when and how many servers to hire, and when to release them, so as to minimize both the job holding costs and the server costs. Under certain assumptions, the problem can be formulated in terms of a semi-Markov decision process and the optimal hiring policy can be computed. Two situations are considered: (a) jobs are submitted in random batches and servers can be hired for arbitrary periods of time; (b) jobs arrive singly and servers must be hired for fixed periods of time. In both cases, the optimal policies are compared with some simple and easily implementable heuristics.

Keywords

Optimal Policy Cloud Server Cloud Provider Greedy Heuristic Batch Arrival 
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.

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References

  1. 1.
    Bennani, M.N., Menascé, D.: Resource allocation for autonomic data centers using analytic performance methods. In: Procs. 2nd IEEE Conf. on Autonomic Computing, ICAC 2005), pp. 229–240 (2005)Google Scholar
  2. 2.
    Bodík, P., Griffith, R., Sutton, C., Fox, A., Jordan, M., Patterson, D.: Statistical machine learning makes automatic control practical for internet datacenters. In: Conf. on Hot Topics in Cloud Computing, HotCloud 2009, Berkeley, CA, USA (2009)Google Scholar
  3. 3.
    Byun, E.-K., Kee, Y.-S., Kim, J.-S., Maeng, S.: Cost optimized provisioning of elastic resources for application workflows. Future Generation Computer Systems 27(8), 1011–1026 (2011), http://dx.doi.org/10.1016/j.future.2011.05.001
  4. 4.
    Byun, E.-K., Kee, Y.-S., Kim, J.-S., Deelman, E., Maeng, S.: BTS: Resource capacity estimate for time-targeted science workflows. Journal of Parallel and Distributed Computing 71(6), 848–862 (2011), doi:10.1016/j.jpdc.2011.01.008CrossRefGoogle Scholar
  5. 5.
    Chandra, A., Gong, W., Shenoy, P.: Dynamic resourse allocation for shared data centers using online measurements. In: Procs. 11th ACM/IEEE Int. Workshop on Quality of Service (IWQoS), pp. 381–400 (2003)Google Scholar
  6. 6.
    Chaisiri, S., Lee, B.S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing 5(2), 164–177 (2012)CrossRefGoogle Scholar
  7. 7.
    Fox, B.L., Glynn, P.W.: Computing Poisson Probabilities. Management Science and Operations Research 31(4), 440–445 (1988)MathSciNetGoogle Scholar
  8. 8.
    Hiden, H., Woodman, S., Watson, P., Cala, J.: Developing cloud applications using the e-science central platform. Royal Soc. of London, Phil. Trans. A. (Mathematical, Physical and Engineering Science), 371 (2013)Google Scholar
  9. 9.
    Lampe, U., Siebenhaar, M., Hans, R., Schuller, D., Steinmetz, R.: Let the clouds compute: Cost-efficient workload distribution in infrastructure clouds. In: Vanmechelen, K., Altmann, J., Rana, O.F. (eds.) GECON 2012. LNCS, vol. 7714, pp. 91–101. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Mazzucco, M., Dyachuk, D., Dikaiakos, M.: Profit-aware server allocation for green internet services. In: IEEE Int. Symp. on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 277–284 (2010)Google Scholar
  11. 11.
    Mazzucco, M., Mitrani, I., Fisher, M., McKee, P.: Allocation and Admission Policies for Service Streams. In: Procs. MASCOTS 2008, Baltimore, pp. 155–162 (2008)Google Scholar
  12. 12.
    Mazzucco, M., Vasar, M., Dumas, M.: Squeezing out the cloud via profit-maximizing resource allocation policies. In: IEEE Int. Symp. on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 19–28 (2012)Google Scholar
  13. 13.
    Mitrani, I.: Managing Performance and Power Consumption in a Server Farm. Annals of Operations Research (2011), doi:10.1007/s10479-011-0932-1Google Scholar
  14. 14.
    Reibman, A., Trivedi, K.: Numerical transient analysis of Markov models. Computing and Operations Research 15(1), 19–36 (1988)CrossRefMATHGoogle Scholar
  15. 15.
    D. Thain, T. Tannenbaum and Miron Livny, “Distributed computing in practice: the Condor experience”, Concurrency and Computation: Practice and Experience, 17 (2-4),323-356, doi:http://dx.doi.org/10.1002/cpe.v17:2/4
  16. 16.
    Tijms, H.C.: Stochastic Models. John Wiley and sons (1994)Google Scholar
  17. 17.
    Urgaonkar, R., Kozat, U.C., Igarashi, K., Neely, M.J.: Dynamic Resource Allocation and Power Management in Virtualized Data Centers. In: IEEE/IFIP NOMS 2010, Osaka, Japan (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrew Stephen McGough
    • 1
  • Isi Mitrani
    • 2
  1. 1.School of Engineering and Computing SciencesDurham UniversityU.K.
  2. 2.School of Computing ScienceNewcastle UniversityU.K.

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