Distributed Service Management Based on Genetic Programming

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

@InProceedings{conf/awic/ChenLW05,
  title =        "Distributed Service Management Based on Genetic
                 Programming",
  author =       "Jing Chen and Zeng-zhi Li and Yun-lan Wang",
  year =         "2005",
  pages =        "83--88",
  editor =       "Piotr S. Szczepaniak and Janusz Kacprzyk and 
                 Adam Niewiadomski",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3528",
  ISBN =         "3-540-26219-9",
  booktitle =    "Advances in Web Intelligence Third International
                 Atlantic Web Intelligence Conference, AWIC 2005,
                 Proceedings",
  address =      "Lodz, Poland",
  month =        "6-9 " # jun,
  bibdate =      "2005-05-30",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/awic/awic2005.html#ChenLW05",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-26219-9",
  DOI =          "doi:10.1007/11495772_14",
  size =         "6 pages",
  abstract =     "An architecture for online discovery quantitative
                 model of distributed service management based on
                 genetic programming (GP) was proposed. The GP system
                 was capable of constructing the quantitative models
                 online without prior knowledge of the managed elements.
                 The model can be updated continuously in response to
                 the changes made in provider configurations and the
                 evolution of business demands. The GP system chose a
                 particular subset from the numerous metrics as the
                 explanatory variables of the model. In order to
                 evaluate the system, a prototype is implemented to
                 estimate the online response times for Oracle Universal
                 Database under a TPC-W workload. Of more than 500
                 Oracle performance metrics, the system model choose
                 three most influential metrics that weight 76percent of
                 the variability of response time, illustrating the
                 effectiveness of quantitative model constructing system
                 and model constructing algorithms.",
}

Genetic Programming entries for Jing Chen Zeng-zhi Li Yun-Lan Wang

Citations