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Service Decomposition and Task Allocation in Distributed Computing Environments

  • Malamati Louta
  • Angelos Michalas
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)

Abstract

Highly competitive and open environments should encompass mechanisms that will assist service providers in accounting for their interests, i.e., offering at a given period of time adequate quality services in a cost efficient manner. Assuming that a user wishes to access a specific service composed of a distinct set of service tasks, which can be served by various candidate service nodes, a problem that should be addressed is the allocation of service tasks to the most appropriate service nodes. This scenario accounts for both the user and the service provider. Specifically, service providers succeed in efficiently managing their resources, while users implicitly exploit in a seamless way the otherwise unutilized power and capabilities of the provider’s network. In general, service task allocation is founded on general and service specific user preferences, service provider’s specific service logic deployment and current system & network load conditions. The pertinent problem is concisely defined, mathematically formulated, optimally solved and evaluated through simulation experiments.

Keywords

Service Provider Communication Cost Mobile Agent Task Allocation Service Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Trigila S., Raatikainen K., Wind B., Reynolds P., 1998. “Mobility in long-term service architectures and distributed platforms”, IEEE Personal Communications, vol. 5, no. 4, pp. 44–55.CrossRefGoogle Scholar
  2. 2.
    Magedanz, T., 1997. “TINA-Architectural basis for future telecommunications services”, Computer Communications, vol. 20, no. 4, pp. 233–245.CrossRefGoogle Scholar
  3. 3.
    Tag M., 1996. “Service creation environment engineering”, Proc. Interworking’96 Conference, Japan.Google Scholar
  4. 4.
    Special Issue, 2003. “Special section on grid computing”, ACM SIGMETRICS Performance Evaluation Review, vol. 30, no. 4, pp. 12–49.CrossRefGoogle Scholar
  5. 5.
    Vinoski S., 1997. “CORBA: Integrating diverse applications within distributed heterogeneous environments”, IEEE Commun. Mag., vol. 35, no. 2, pp. 46–55.CrossRefGoogle Scholar
  6. 6.
    Morreale P., 1998. “Agents on the move”, IEEE Spectrum, vol. 35, no. 4, pp. 34–41.CrossRefMathSciNetGoogle Scholar
  7. 7.
    Papadimitriou C, Steiglitz K, 1982. Combinatorial optimization: Algorithms and complexity. Prentice Hall, Inc.Google Scholar
  8. 8.
    The Voyager Platform, Recursion Software Inc. http://www.recursionsw.com/Google Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Malamati Louta
    • 1
  • Angelos Michalas
    • 2
    • 3
  1. 1.Department of Business AdministrationTechnological Educational Institute of Western MacedoniaKoila, KozaniGreece
  2. 2.Department of Information and Communication Technologies EngineeringUniversity of Western MacedoniaGreece
  3. 3.Department of Informatics and Computer TechnologyTechnological Educational Institute of Western MacedoniaFourka, KastoriaGreece

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