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Achieving Market Liquidity Through Autonomic Cloud Market Management

Conference paper
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Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)

Abstract

Due to the large variety in computing resources, Cloud markets often suffer from a low probability of finding matches between consumers’ bids and providers’ asks, resulting in low market liquidity. The approach of service level agreement (SLA) templates (i.e., templates for electronic contracts) is a mean to reduce this variety as it channels the demand and supply. However, until now, the SLA templates used were static, not able to reflect changes in users’ requirements. To address this shortcoming, we introduce an adaptive approach for automatically deriving public SLA templates based on the requirements of market participants. To achieve this goal, we utilize clustering algorithms for grouping similar requirements and learning methods for adapting the public SLA templates to observed changes of market conditions. To assess the benefits of the approach, we conduct a simulation-based evaluation and formalize a utility and cost model. Our results show that the use of clustering algorithms and learning algorithms improves the performance of the adaptive SLA template approach.

Keywords

Service level agreement SLA management Electronic markets Autonomic computing Marketplace Market environments 

Notes

Acknowledgements

This work is supported by the Vienna Science and Technology Fund under the grant agreement ICT08-018 Foundations of Self-Governing ICT Infrastructures (FoSII), and by the National Research Foundation of the Ministry of Education, Science and Technology of Korea under the grant K21001001625-10B1300-03310.

References

  1. 1.
    Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25 (2009) 599–616CrossRefGoogle Scholar
  2. 2.
    Risch, M., Brandic, I., Altmann, J.: Using SLA mapping to increase market liquidity. In: Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops. Volume 6275 of Lecture Notes in Computer Science. Springer (2010) 238–247Google Scholar
  3. 3.
    Samuelson, P., Nordhaus, W.: Economics. 18. ed., internat. ed. edn. McGraw-Hill/Irwin (2005)Google Scholar
  4. 4.
    Brandic, I., Music, D., Dustdar, S.: VieSLAF framework: Facilitating negotiations in clouds by applying service mediation and negotiation bootstraping (2010)Google Scholar
  5. 5.
    Oldham, N., Verma, K.: Semantic WS-agreement partner selection. In: 15th international conference on World Wide Web. WWW ’06, ACM Press (2006) 697–706Google Scholar
  6. 6.
    Dobson, G., Sanchez-Macian, A.: Towards unified QoS/SLA ontologies. In: Services Computing Workshops, 2006. SCW ’06. IEEE. (2006) 169 –174Google Scholar
  7. 7.
    Green, L.: Service level agreements: an ontological approach. In: 8th international conference on Electronic commerce. ICEC ’06, ACM (2006) 185–194Google Scholar
  8. 8.
    Karänke, P., Kirn, S.: Service level agreements: An evaluation from a business application perspective. In: eChallenges e-2007, IEEE-CS Press (2007) 104–111Google Scholar
  9. 9.
    Buyya, R., Abramson, D., Giddy, J.: A case for economy grid architecture for service oriented grid computing. Parallel and Distributed Processing Symposium 2 (2001)Google Scholar
  10. 10.
    Nimis, J., Anandasivam, A., Borissov, N., Smith, G., Neumann, D., Wirstrs̆m, N., Rosenberg, E., Villa, M.: SORMA: Business cases for an open grid market: Concept and implementation. In: Grid Economics and Business Models. Volume 5206 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg (2008) 173–184Google Scholar
  11. 11.
    Neumann, D., Stösser, J., Weinhardt, C.: Bridging the adoption gap-developing a roadmap for trading in grids. Electronic Markets 18 (2008) 65–74CrossRefGoogle Scholar
  12. 12.
    Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, AAAI Press (1996) 226–231Google Scholar
  13. 13.
    MacQueen, J. B.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability. Volume 1., University of California Press (1967) 281–297Google Scholar
  14. 14.
    Mardia, K. V., Kent, J. T.: Multivariate Analysis. Academic Press (1980)Google Scholar
  15. 15.
    Hartigan, J. A.: Clustering Algorithms. John Wiley & Sons Inc (1975)Google Scholar
  16. 16.
    Maurer, M., Emeakaroha, V. C., Risch, M., Brandic, I., Altmann, J.: Cost and benefit of the SLA mapping approach for defining standardized goods in cloud computing markets. In: International Conference on Utility and Cloud Computing (UCC 2010) in conjunction with the International Conference on Advanced Computing (ICoAC 2010). (2010)Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Distributed Systems GroupVienna University of TechnologyViennaAustria
  2. 2.TEMEP, College of EngineeringSeoul National UniversitySeoulSouth Korea

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