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Dynamic provisioning in multi-tenant service clouds

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Abstract

Cloud-based systems promise an on-demand service provisioning system along with a “pay-as-you-use” policy. In the case of multi-tenant systems this would mean dynamic creation of a tenant by integrating existing cloud-based services on the fly. Presently, dynamic creation of a tenant is handled by building the required components from scratch. Although multi-tenant systems help providers save cost by allocating multiple tenants to the same instance of an application, they incur huge reconfiguration costs. Cost and time spent on these reconfiguration activities can be reduced by re-constructing tenants from existing tenant configurations supported by service providers. Multi-tenant cloud-based systems also lack the facility of allowing clients to specify their requirements. Giving clients the flexibility to specify requirements helps them avoid spending an excessive amount of time and effort looking through a list of services, many of which might not be relevant to them. Moreover, dynamic provisioning in the cloud requires an integrated solution across the technology stack (software, platform and infrastructure) combining functional, non-functional and resource allocation requirements. Existing research works in the area of web service matching, although numerous, still fall short, since they usually consider each requirement type in isolation and cannot provide an integrated solution. To that end, in this paper we investigate the features needed for dynamic service provisioning on the cloud. We propose a novel User Interface-Tenant Selector-Customizer (UTC) model and approach, which enables cloud-based services to be systematically modeled and provisioned as variants of existing service tenants in the cloud. Our approach considers functional, non-functional and resource allocation requirements, which are explicitly specified by the client via the user interface component of the model. To the best of our knowledge, ours is the first such integrated approach. We illustrate our ideas using a realistic running example, and also present a proof-of-concept prototype built using IBM’s Rational Software Architect modeling tool. We also present experimental results demonstrating the applicability of our matching algorithm. Our results show significant reduction in matching time with the help of an elimination process that reduces the search space needed for performing matching.

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References

  1. Agarwal V, Dasgupta K, Karnik NM, Kumar A, Kundu A, Mittal S, Srivastava B (2005) A service creation environment based on end to end composition of web services. In: WWW, pp 128–137

  2. Aoun R, Doumith EA, Gagnaire M (2010) Resource provisioning for enriched services in cloud environment. In: CloudCom, pp 296–303

  3. Arnold W, Eilam T, Kalantar MH, Alexander V (2007) Konstantinou, and Alexander Totok. Pattern based soa deployment. In: ICSOC, pp 1–12

  4. Bellur U, Kulkarni R (2007) Improved matchmaking algorithm for semantic web services based on bipartite graph matching. In: ICWS, pp 86–93

  5. Bellur U, Vadodaria H (2008) On extending semantic matchmaking to include preconditions and effects. In: ICWS, pp 120–128

  6. Cardoso J, Sheth AP, Miller JA, Arnold J, Kochut K (2004) Quality of service for workflows and web service processes. J Web Sem 1(3): 281–308

    Article  Google Scholar 

  7. Cloud Computing Use Cases, accessed 19th August 2010. http://groups.google.com/group/cloud-computing-use-cases?pli=1

  8. Chao K-M,Younas M, Griffiths N, Awan I, Anane R, Tsai C-F (2004) Analysis of grid service composition with bpel4ws. In: AINA (1), pp 284–289

  9. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Algorithms, Second Edition. The MIT Press and McGraw-Hill Book Company

  10. Deutsch A, Vianu V (2008) Wave: automatic verification of data-driven web services. IEEE Data Eng Bull 31(3): 35–39

    Google Scholar 

  11. Ezenwoye O, Sadjadi SM, Cary A, Robinson M (2007) Grid service composition in bpel for scientific applications. In: OTM workshops, pp 1304–1312

  12. Glatard T, Montagnat J, Pennec X (2006) Efficient services composition for grid-enabled data-intensive applications. In: HPDC, pp 333–334

  13. Groher I, Voelter M (2007) Expressing feature-based variability in structural models. In: SPLC ’07: proceedings of the workshop on managing variability for software product lines

  14. Horrocks I (2008) Ontologies and the semantic web. Commun ACM 51(12): 58–67

    Article  Google Scholar 

  15. Huhns MN, Singh MP (2005) Service-oriented computing: key concepts and principles. IEEE Internet Comput 9(1): 75–81

    Article  Google Scholar 

  16. Konstantinou AV, Eilam T, Kalantar M, Totok AA, Arnold W, Snible E (2009) An architecture for virtual solution composition and deployment in infrastructure clouds. In: VTDC ’09: Proceedings of the 3rd international workshop on virtualization technologies in distributed computing. ACM, New York, NY, USA, pp 9–18

  17. Lenk A, Klems M, Jens N, Tai S, Sandholm T (2009) What’s inside the cloud? an architectural map of the cloud landscape. In: ICSE cloud 09: first international workshop on software engineering challenges for cloud computing, pp 23–31

  18. Li L, Horrocks I (2003) A software framework for matchmaking based on semantic web technology. In: WWW 2003. ACM, New York, NY, USA, pp 331–339

  19. Maedche A, Staab S (2004) Ontology learning. In: Handbook on ontologies, pp 173–190

  20. Mietzner R, Metzger A, Leymann F, Pohl K (2009) Variability modeling to support customization and deployment of multi-tenant-aware software as a service applications. In: PESOS ’09: proceedings of the 2009 ICSE workshop on Principles of engineering service oriented systems. IEEE Computer Society, Washington, DC, USA, pp 18–25

  21. Paolucci M, Kawamura T, Payne TR, Sycara KP (2002) Semantic matching of web services capabilities. In: International semantic web conference, pp 333–347

  22. Pautasso C (2005) Jopera: an agile environment for web service composition with visual unit testing and refactoring. In: VL/HCC, pp 311–313

  23. Sam Y, Boucelma O, Hacid M-S (2006) Web services customization: a composition-based approach. In: ICWE ’06: proceedings of the 6th international conference on web engineering. ACM, New York, NY, USA, pp 25–31

  24. Sheth AP, Gomadam K, Ranabahu A (2008) Semantics enhanced services: Meteor-s, sawsdl and sa-rest. IEEE Data Eng Bull 31(3): 8–12

    Google Scholar 

  25. ShvaikoPavel Euzenat J (2005) A survey of schema-based matching approaches. J Data Semant IV 146(171): 146–171

    Article  Google Scholar 

  26. Vaculín R, Neruda R, Sycara KP (2008) Towards extending service discovery with automated composition capabilities. In: ECOWS, pp 3–12

  27. Vaquero LM, Rodero-Merino L, Buyya R (2011) Dynamically scaling applications in the cloud. Comput Commun Rev 41(1): 45–52

    Article  Google Scholar 

  28. Venugopal S, Buyya R, Winton LJA (2006) grid service broker for scheduling e-science applications on global data grids. Concurr Comput Pract Exp 18(6): 685–699

    Article  Google Scholar 

  29. Jia Y, Venugopal S, Buyya R (2006) A market-oriented grid directory service for publication and discovery of grid service providers and their services. J Supercomput 36(1): 17–31

    Article  Google Scholar 

  30. Jing Z, Guosun Z (2009) A mechanism for grid service composition behavior specification and verification. Future Gener Comput Syst 25: 378–383

    Article  Google Scholar 

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, USA

    Lakshmi Ramachandran

  2. IBM India Research Lab, Bangalore, India

    Nanjangud C. Narendra & Karthikeyan Ponnalagu

Authors
  1. Lakshmi Ramachandran
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  2. Nanjangud C. Narendra
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  3. Karthikeyan Ponnalagu
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Corresponding author

Correspondence to Nanjangud C. Narendra.

Additional information

Thanks to the following for their feedback: Munindar Singh, Marco Aiello, GR Gangadharan, Saurabh Sinha, Vinayaka Pandit, Sugata Ghosal and Debdoot Mukherjee.

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Ramachandran, L., Narendra, N.C. & Ponnalagu, K. Dynamic provisioning in multi-tenant service clouds. SOCA 6, 283–302 (2012). https://doi.org/10.1007/s11761-012-0116-0

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  • DOI: https://doi.org/10.1007/s11761-012-0116-0

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