Cloud Flat Rates Enabled via Fair Multi-resource Consumption

  • Patrick Poullie
  • Burkhard Stiller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9701)


Many companies rent Virtual Machines (VM) from cloud providers to meet their computational needs. While this option is also available to end-users, they do not always take advantage of this option. One reason may be that it is common to pay on a per-VM-basis, whereas the telecommunications sector has shown that customers prefer flat rates. A flat rate for cloud services needs to define utilization thresholds, to cap the usage of heavy customers and thereby limit their impact on the flat rate price and the cloud performance. Unfortunately, customers consume multiple heterogenous resources in clouds, e.g., CPU, RAM, disk I/O and space, or network access. This makes the definition of a customer’s fair “cloud share” and according utilization thresholds complex.

Backed by a questionnaire among more than 600 individuals, this paper designs the new Greediness Metric (GM) that formalizes an intuitive understanding of multi-resource fairness without access to consumers’ utility functions. This GM enables the introduction of attractive cloud flat rates and fair sharing policies for private/commodity clouds and provides incentive to customers to wisely determine VM configurations.


  1. 1.
    Altmann, J., Rupp, B., Varaiya, P.: Effects of pricing on internet user behavior. Netnomics 3(1), 67–84 (2001)CrossRefGoogle Scholar
  2. 2.
    Amazon Web Services, Inc., Amazon EC2 Pricing (2016). Accessed 26 Jan 2016
  3. 3.
    Arcangeli, A., Eidus, I., Wright, C.: Increasing memory density by using KSM. In: 2009 Linux Symposium, vol. 1, pp. 19–28, Montreal, QC, Canada, July 2009Google Scholar
  4. 4.
    Bonald, T., Roberts, J.: Enhanced cluster computing performance through proportional fairness. Perform. Eval. 79, 134–145 (2014)CrossRefGoogle Scholar
  5. 5.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  6. 6.
    Chandra, A., Adler, M., Goyal, P., Shenoy, P.: Surplus fair scheduling: a proportional-share CPU scheduling algorithm for symmetric multiprocessors. In: 4th Conference on Symposium on Operating System Design & Implementation, OSDI 2000, San Diego, CA, USA, p. 4, October 2000Google Scholar
  7. 7.
    Dolev, D., Feitelson, D.G., Halpern, J.Y., Kupferman, R., Linial, N.: No Justified complaints: on fair sharing of multiple resources. In: 3rd Innovations in Theoretical Computer Science Conference, ITCS 2012, Cambridge, MA, USA, pp. 68–75, January 2012Google Scholar
  8. 8.
    Etsion, Y., Ben-Nun, T., Feitelson, D.G.: A global scheduling framework for virtualization environments. In: 2009 IEEE International Symposium on Parallel Distributed Processing, IPDPS 2009, Rome, Italy, pp. 1–8, May 2009Google Scholar
  9. 9.
    Ghodsi, A., Zaharia, M., Hindman, B., Konwinski, A., Shenker, S., Stoica, I.: Dominant resource fairness: fair allocation of heterogeneous resources in data centers. Technical report UCB/EECS-2010-55, EECS Department, University of California, Berkeley, May 2010Google Scholar
  10. 10.
    Guo, F.: Understanding Memory Resource Management in VMware vSphere 5.0. In: Performance study, VMware, Palo Alto, CA, USA, (2011).
  11. 11.
    Gutman, A., Nisan, A.: Fair allocation without trade. In: 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, vol. 2, pp. 719–728, Valencia, Spain, June 2012Google Scholar
  12. 12.
    IBM: Best Practices for KVM. Technical report, Austin, TX, USA, November 2010.
  13. 13.
    Jackson, K.: OpenStack Cloud Computing Cookbook. Packt Publishing, Birmingham (2012)Google Scholar
  14. 14.
    Jain, R.K., Chiu, D.-M.W., Hawe, W.R.: A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. Technical report TR-301, Digital Equipment Corp, Hudson, MA, USA, September 1984Google Scholar
  15. 15.
    Joe-Wong, C., Sen, S., Lan, T., Chiang, M.: Multi-resource allocation: fairness-efficiency tradeoffs in a unifying framework. In: 31st Annual IEEE International Conference on Computer Communications, INFOCOM 2012, pp. 1206–1214, Orlando, FL, USA, March 2012Google Scholar
  16. 16.
    Kapil, D., Pilli, E.S., Joshi, R.C.: Live virtual machine migration techniques: survey and research challenges. In: IEEE 3rd International Advance Computing Conference, IACC 2013, pp. 963–969, Ghaziabad, UP, India, February 2013Google Scholar
  17. 17.
    Klusáček, D., Rudová, H., Jaroš, M.: Multi resource fairness: problems and challenges. In: Desai, N., Cirne, W. (eds.) JSSPP 2013. LNCS, vol. 8429, pp. 81–95. Springer, Heidelberg (2014)Google Scholar
  18. 18.
    Lambrecht, A., Skiera, B.: Paying too much and being happy about it: existence, causes, and consequences of tariff-choice biases. J. Mark. Res. 43(2), 212–223 (2006)CrossRefGoogle Scholar
  19. 19.
    Lan, T., Kao, D., Chiang, M., Sabharwal, A.: An axiomatic theory of fairness in network resource allocation. In: 29th Annual IEEE International Conference on Computer Communications, INFOCOM 2010, San Diego, CA, USA, pp. 1–9, March 2010Google Scholar
  20. 20.
    Lee, G., Chun, B.-G., Katz, R.H.: Heterogeneity-aware resource allocation and scheduling in the cloud. In: 3rd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2011, Portland, OR, USA, p. 4, June 2011Google Scholar
  21. 21.
    Levinson, D., Odlyzko, A.: Too Expensive to meter: the influence of transaction costs in transportation and communication. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 366(1872), 2033–2046 (2008)CrossRefzbMATHGoogle Scholar
  22. 22.
    Odlyzko, A.: Internet pricing and the history of communications. Comput. Netw. 36(5–6), 493–517 (2001)CrossRefGoogle Scholar
  23. 23.
    Parkes, D.C., Procaccia, A.D., Shah, N.: Beyond dominant resource fairness: extensions, limitations, and indivisibilities. In: 13th ACM Conference on Electronic Commerce, EC 2012, Valencia, Spain, pp. 808–825, June 2012Google Scholar
  24. 24.
    Poullie, P., Stiller, B.: Cloud flat rates enabled via fair multi-resource consumption. Technical report IFI-2015.03, Universität Zürich, Zurich, Switzerland, October 2015.
  25. 25.
    Floyd, S. (ed.): Metrics for the evaluation of congestion control mechanisms. RFC 5166, IETF, Berkeley, CA, USA, March 2008Google Scholar
  26. 26.
    Strunk, A.: Costs of virtual machine live migration: a survey. In: 2012 IEEE 8th World Congress on Services, SERVICES 2012, Honolulu, HI, USA, pp. 323–329, June 2012Google Scholar
  27. 27.
    Wei, G., Vasilakos, A., Zheng, Y., Xiong, N.: A Game-theoretic method of fair resource allocation for cloud computing services. J. Supercomput. 54(2), 252–269 (2010)CrossRefGoogle Scholar
  28. 28.
    Zahedi, S.M., Lee, B.C.: REF: resource elasticity fairness with sharing incentives for multiprocessors. In: 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2014, Salt Lake City, UT, USA, pp. 145–160, March 2014Google Scholar
  29. 29.
    Zeldes, Y., Feitelson, D.G.: On-line fair allocations based on bottlenecks and global priorities. In: 4th ACM/SPEC International Conference on Performance Engineering, ICPE 2013, Prague, Czech Republic, pp. 229–240, April 2013Google Scholar
  30. 30.
    Zukerman, M., Tan, L., Wang, H., Ouveysi, I.: Efficiency-fairness tradeoff in telecommunications networks. IEEE Commun. Lett. 9(7), 643–645 (2005)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Communication Systems Group (CSG), Department of Informatics (IfI)University of ZürichZürichSwitzerland

Personalised recommendations