A Genetic Programming Approach to Distributed QoS-Aware Web Service Composition

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Yu:2014:CECe,
  title =        "A Genetic Programming Approach to Distributed
                 {QoS}-Aware Web Service Composition",
  author =       "Yang Yu and Hui Ma and Mengjie Zhang",
  pages =        "1840--1846",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming, Parallel and
                 Distributed Evolutionary Computation in the Cloud Era",
  DOI =          "doi:10.1109/CEC.2014.6900416",
  abstract =     "Web service composition has emerged as a promising
                 technique for building complex web applications, thus
                 supporting business-to-business and enterprise
                 application integration. Nowadays there are increasing
                 numbers of web services are distributed across the
                 Internet. For a given service request there are many
                 ways of service composition that can meet the service
                 functional requirements (inputs and outputs) but have
                 different qualities of Services (QoS), like response
                 time or execution cost. QoS-aware web service
                 composition seeks to find a service composition with
                 optimised QoS properties. Genetic Programming is an
                 efficient tool for tacking such optimisation problems
                 efficiently. This paper proposes a novel GP-based
                 approach for distributed web service composition where
                 multiple QoS constraints are considered simultaneously.
                 A series of experiments have been conducted to evaluate
                 the proposed approach with test data. The results show
                 that our approach is efficient and effective to find a
                 near-optimal service composition solution in the
                 context of distributed service environment.",
  notes =        "Estimates response time (latency) using GNP global
                 network positioning of of web server. GP tree composed
                 of sequence, choice, parallel loop with leafs being
                 atomic web services. Pop 50, generations
                 500.

                 WCCI2014",
}

Genetic Programming entries for Yang Yu Hui Ma Mengjie Zhang

Citations