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Resource Calculations with Constraints, and Placement of Tenants and Instances for Multi-tenant SaaS Applications

  • Thomas Kwok
  • Ajay Mohindra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5364)

Abstract

Cost of customization, deployment and operation of a software application supporting multiple tenants can be lowered through multi-tenancy in a new application business model called Software as a Service (SaaS). However, there are a number of technical challenges that need to be tackled before these benefits can be realized. These challenges include calculations of resource requirements for multi-tenants with applied constraints in a shared application instance, the optimal placement of tenants and instances with maximum cost savings but without violating any requirements of service level agreements for all tenants in a set of servers. Moreover, previously reported capacity planning and resource allocation methods and tools are not tenant aware. This paper will address and provide novel solutions to these challenges. We also describe the first of a kind, a multi-tenant placement tool for application deployment in a distributed computing environment.

Keywords

capacity planning resource allocation and management tenant placement constraint multi-tenant software as a service SaaS 

References

  1. 1.
    Iod: Sotware as a Service, Director Publications Ltd., London (2002)Google Scholar
  2. 2.
    Iyar, S.: Why Buy the Cow, Santa Clara (2007)Google Scholar
  3. 3.
    Kobilsky, N.: SAP CRM on-demand, SAP Forum (2006)Google Scholar
  4. 4.
    Gianforte, G.: Multiple-Tenancy Hosted Applications: The Death and Rebirth of the Software Industry. RightNow Technologies Inc. (2005), http://wwww.rightnow.com
  5. 5.
    Chong, F., Gianpaolo, C., Wolter, R.: Multi-Tenant Data Architecture, Microsoft Corporation (2006), http://www.msdn2.microsoft.com/
  6. 6.
    Fisher, S.: The Architecture of the Apex Platform, salesforce.com’s Platform for Building On-Demand Applications. In: Proc. of the 29th IEEE Int’l Conference on Software Engineering, p. 3. IEEE Press, New York (2007)Google Scholar
  7. 7.
    Guo, J.G., Sun, W., Huang, Y., Wang, Z.H., Gao, B.: A Framework for Native Multi-Tenancy Application Development and Management. In: Proc. of the 9th IEEE Int’l Conference on E-Commerce Technology, pp. 551–558. IEEE Press, New York (2007)Google Scholar
  8. 8.
    Kwok, T., Nguyen, T., Lam, L.: A Software as a Service with Multi-Tenancy Support for an Electronic Contract Application. In: Proc. of IEEE Int’l Conference on Services Computing, pp. 28–33. IEEE Press, New York (2008)Google Scholar
  9. 9.
    Mendoza, A.: Utility Computing Technologies, Standards, and Strategies. Artech House Publishers, Norwood (2007)Google Scholar
  10. 10.
    Herroelen, W., Reyck, B.D., Demeulemeester, E.: Resource-Constrained Project Scheduling: A Survey of Recent Developments. Computers and Operations Research 25(4), 279–302 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Brucker, P., Drexel, A., Mohring, R., Neumann, K., Pesch, E.: Resource-Constrained Project Scheduling: Notation, Classification, Models and Methods. European Journal of Operational Research 112, 3–41 (1999)CrossRefzbMATHGoogle Scholar
  12. 12.
    Hartmann, S., Kolisch, R.: Experimental Evaluation of State-of-the-Art Heuristics for Resource-Constrained Project Scheduling Problem. European Journal of Operational Research 127, 394–407 (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Kolisch, R.: Efficient Priority Rules for the Resource-Constrained Project Scheduling Problem. Journal of Operations Management 14, 179–192 (1996)CrossRefGoogle Scholar
  14. 14.
    Bouleimen, K., Lecocq, H.: A New Efficient Simulated Annealing Algorithm for the Resource-Constrained Project Scheduling Problem and its Multiple Mode Version. European Journal of Operational Research 149, 268–281 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Rajkumar, R., Lee, C., Lehoczky, J., Siewiorek, D.: A Resource Allocation Model for QoS Management. In: Proc. of the 18th IEEE Real-Time Systems Symposium, pp. 298–307. IEEE Press, New York (1997)Google Scholar
  16. 16.
    Islam, N., Prodromidis, A., Squillante, M., Fong, L., Gopal, A.: Extensible Resource Management for Cluster Computing. In: Proc. of the 17th Int’l Conference on Distributed Computing Systems, pp. 561–568. IEEE Press, New York (1997)CrossRefGoogle Scholar
  17. 17.
    Bagchi, S., Hung, E., Iyengar, A., Vogl, N., Wadia, N.: Capacity Planning Tools for Web and Grid Environments. In: Proc. of the 1st Int’l Conference on Performance Evaluation Methodologies and Tools, pp. 25–34. ACM Press, New York (2006)Google Scholar
  18. 18.
    Bhatti, M.A.: Practical Optimization Methods. Springer, New York (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas Kwok
    • 1
  • Ajay Mohindra
    • 1
  1. 1.IBM Research DivisionThomas J. Watson Research CenterHawthorne

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