Advertisement

A Fuzzy Approach to Cloud Admission Control for Safe Overbooking

  • Carlos Vázquez
  • Luis Tomás
  • Ginés Moreno
  • Johan Tordsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)

Abstract

Cloud computing enables elasticity - rapid provisioning and deprovisioning of computational resources. Elasticity allows cloud users to quickly adapt resource allocation to meet changes in their workloads. For cloud providers, elasticity complicates capacity management as the amount of resources that can be requested by users is unknown and can vary significantly over time. Overbooking techniques allow providers to increase utilization of their data centers. For safe overbooking, cloud providers need admission control mechanisms to handle the tradeoff between increased utilization (and revenue), and risk of exhausting resources, potentially resulting in penalty fees and/or lost customers. We propose a flexible approach (implemented with fuzzy logic programming) to admission control and the associated risk estimation. Our measures exploit different fuzzy logic operators in order to model optimistic, realistic, and pessimistic behaviour under uncertainty. The application has been coded with the MALP language by using the FLOPER system developed in our research group. An experimental evaluation confirm that our fuzzy admission control approach can significantly increase resource utilization while minimizing the risk of exceeding the total available capacity.

Keywords

Cloud Computing Admission Control Fuzzy Logic Programming Resource Utilization Risk Assessment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G.: Fuzzy logic programming for implementing a flexible xpath-based query language. Electronic Notes in Theoretical Computer Science 282, 3–18 (2012)CrossRefGoogle Scholar
  2. 2.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G.: A xpath debugger based on fuzzy chance degrees. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM-WS 2012. LNCS, vol. 7567, pp. 669–672. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G., Vázquez, C.: Analyzing fuzzy logic computations with fuzzy xpath. In: Fredlund, A. (ed.) Proc. of XIII Spanish Conference on Programming and Languages, PROLE 2013, Madrid, Spain, September 18-20, p. 15. ECEASST (to appear, 2013)Google Scholar
  4. 4.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G.: A Flexible XPath-based Query Language Implemented with Fuzzy Logic Programming. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 186–193. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Almendros-Jiménez, J.M., Luna, A., Moreno, G.: Annotating Fuzzy Chance Degrees when Debugging Xpath Queries. In: Rojas, I., Joya, G., Cabestany, J. (eds.) IWANN 2013, Part II. LNCS, vol. 7903, pp. 300–311. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Amazon Elastic Compute Cloud (Amazon EC2), http://aws.amazon.com/ec2/ (visited July 30, 2013)
  7. 7.
    Barham, P., Dragovic, B., et al.: Xen and the art of virtualization. SIGOPS Oper. Syst. Rev. 37(5), 164–177 (2003)CrossRefGoogle Scholar
  8. 8.
    Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems 24(7), 1366–1379 (2013)CrossRefGoogle Scholar
  9. 9.
    Breitgand, D., Dubitzky, Z., Epstein, A., Glikson, A., Shapira, I.: SLA-aware resource over-commit in an IaaS cloud. In: Proc. of the 8th Intl. Conference on Network and Service Management (CNSM), pp. 73–81 (2012)Google Scholar
  10. 10.
    FLOPER - A Fuzzy LOgic Programming Environment for Research, http://dectau.uclm.es/floper/ (Visited June 7, 2013)
  11. 11.
    Julián, P., Moreno, G., Penabad, J.: Operational/Interpretive Unfolding of Multi-adjoint Logic Programs. Journal of Universal Computer Science 12(11), 1679–1699 (2006)Google Scholar
  12. 12.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Trends in logic, Studia logica library. Springer (2000)Google Scholar
  13. 13.
    Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Berlin (1987)CrossRefzbMATHGoogle Scholar
  14. 14.
    Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Similarity-based Unification: A multi-adjoint approach. Fuzzy Sets and Systems 146, 43–62 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Morcillo, P.J., Moreno, G.: Programming with fuzzy logic rules by using the FLOPER tool. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2008. LNCS, vol. 5321, pp. 119–126. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Morcillo, P.J., Moreno, G.: Modeling interpretive steps in fuzzy logic computations. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds.) WILF 2009. LNCS (LNAI), vol. 5571, pp. 44–51. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Morcillo, P.J., Moreno, G., Penabad, J., Vázquez, C.: A Practical Management of Fuzzy Truth Degrees using FLOPER. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 20–34. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Morcillo, P.J., Moreno, G., Penabad, J., Vázquez, C.: Fuzzy Computed Answers Collecting Proof Information. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part II. LNCS, vol. 6692, pp. 445–452. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Rochwerger, B., Breitgand, D., et al.: The Reservoir model and architecture for open federated cloud computing. IBM J. Res. Dev. 53(4), 535–545 (2009)CrossRefGoogle Scholar
  20. 20.
    Schweizer, B., Sklar, A.: Probabilistic Metric Spaces. Courier Dover Publ. (1983)Google Scholar
  21. 21.
    The NIST Definition of Cloud Computing, http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (visited July 30, 2013)
  22. 22.
    Tomás, L., Tordsson, J.: Improving Cloud Infrastructure Utilization through Overbooking. In: Proc. of the ACM Cloud and Autonomic Computing Conference, CAC (to appear, 2013)Google Scholar
  23. 23.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Zaharia, M., Hindman, B., et al.: The datacenter needs an operating system. In: Proc. of the 3rd USENIX Conference on Hot Topics in Cloud Computing, p. 17 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Carlos Vázquez
    • 1
  • Luis Tomás
    • 2
  • Ginés Moreno
    • 1
  • Johan Tordsson
    • 2
  1. 1.Dept. of Computing SystemsUniversity of Castilla-La ManchaSpain
  2. 2.Dept. of Computing ScienceUmeå UniversitySweden

Personalised recommendations