Dynamic SLAs for Clouds

  • Rafael Brundo Uriarte
  • Francesco Tiezzi
  • Rocco De Nicola
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9846)

Abstract

In the Cloud domain, to guarantee adaptation to the needs of users and providers, Service-Level-Agreements (SLAs) would benefit from mechanisms to capture the dynamism of services. The existing SLA languages attempt to address this challenge by focusing on renegotiation of the agreement terms, which is a heavy-weight process, not really suitable for dealing with cloud dynamism. In this paper, we propose an extension of SLAC, a SLA language for clouds that we have recently defined, with a mechanism that enable dynamic modifications of the service agreement. We formally describe this extension, implement it in the SLAC framework and analyse the impacts of dynamic SLAs in some applications. The advantages of dynamic SLAs are demonstrated by comparing their effect with that of static SLA and of the “renegotiation” approach.

References

  1. 1.
    SLAC: A Formal Service-Level-Agreement Language for Cloud Computing (2016). http://sysma.imtlucca.it/tools/slac/
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Di Modica, G., Regalbuto, V., Tomarchio, O., Vita, L., Doria, V.A.: Dynamic re-negotiations of SLA in service composition scenarios. In: SEAA, pp. 359–366. IEEE (2007)Google Scholar
  4. 4.
    Di Modica, G., Tomarchio, O., Vita, L.: Dynamic SLAs management in service oriented environments. J. Syst. Softw. 82(5), 759–771 (2009)CrossRefGoogle Scholar
  5. 5.
    Djemame, S.S.K.: Enabling service-level agreement renegotiation through extending WS-Agreement specification. SOCA 9, 177–191 (2015)CrossRefGoogle Scholar
  6. 6.
    Galati, A., Djemame, K., Fletcher, M., Jessop, M., Weeks, M., Hickinbotham, S., McAvoy, J.: Designing an SLA protocol with renegotiation to maximize revenues for the CMAC platform. In: Haller, A., Huang, G., Huang, Z., Paik, H., Sheng, Q.Z. (eds.) WISE 2011 and 2012. LNCS, vol. 7652, pp. 105–117. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Garg, R., Saran, H., Randhawa, R., Singh, M.: A SLA framework for QoS provisioning and dynamic capacity allocation. In: IWQoS, pp. 129–137. IEEE (2002)Google Scholar
  8. 8.
    Green, L.: Service level negotiation in a heterogeneous telecommunication environment. In: I4CT. IEEE (2004)Google Scholar
  9. 9.
    Lee, C.A., Sill, A.F.: A design space for dynamic service level agreements in OpenStack. J. Cloud Comput. 3(1), 17 (2014)CrossRefGoogle Scholar
  10. 10.
    Nanduri, R., Maheshwari, N., Reddyraja, A., Varma, V.: Job aware scheduling algorithm for mapreduce framework. In: CloudCom, pp. 724–729. IEEE (2011)Google Scholar
  11. 11.
    Omezzine, A., Tazi, S., Bellamine, N., Saoud, B., Drira, K., Cooperman, G.: Towards a dynamic multi-level negotiation framework in cloud computing. In: CloudTech. IEEE (2015)Google Scholar
  12. 12.
    Parkin, M., Kuo, D., Brooke, J., MacCulloch, A.: Challenges in EU grid contracts. In: Exploiting the Knowledge Economy: Issues, Applications and Case Studies, pp. 67–75. IOS Press (2006)Google Scholar
  13. 13.
    Parkin, M., Hasselmeyer, P., Koller, B.: An SLA re-negotiation protocol. In: NFPSLA-SOC (2008)Google Scholar
  14. 14.
    Pichot, A., Wäldrich, O., Ziegler, W., Wieder, P.: Towards dynamic service level agreement negotiation: an approach based on WS-Agreement. In: WEBIST, pp. 107–119. SCITEPRESS (2009)Google Scholar
  15. 15.
    Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema. Technical report, Google Inc. November 2011. http://googleclusterdata.googlecode.com/files/Googlecluster-usagetraces-format+schema(2011.10.27external).pdf
  16. 16.
    Shen, W., Li, Y., Ghenniwa, H., Wang, C.: Adaptive negotiation for agent-based grid computing. JASA 97(457), 210–214 (2002)CrossRefGoogle Scholar
  17. 17.
    Uriarte, R.B., Tiezzi, F., De Nicola, R.: SLAC: a formal service-level-agreement language for cloud computing. In: UCC, pp. 419–426. IEEE (2014)Google Scholar
  18. 18.
    Uriarte, R.B., Tsaftaris, S., Tiezzi, F.: Service clustering for autonomic clouds using random forest. In: CCGrid, pp. 515–524. IEEE (2015)Google Scholar
  19. 19.
    Uriarte, R.B., Westphall, C.B.: Panoptes: a monitoring architecture and framework for supporting autonomic clouds. In: NOMS. IEEE (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Rafael Brundo Uriarte
    • 1
  • Francesco Tiezzi
    • 2
  • Rocco De Nicola
    • 1
  1. 1.IMT School for Advanced Studies LuccaLuccaItaly
  2. 2.University of CamerinoCamerinoItaly

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