Evolutionary design of en-route caching strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Jurgen Branke and Pablo Funes and Frederik Thiele",
  title =        "Evolutionary design of en-route caching strategies",
  journal =      "Applied Soft Computing",
  year =         "2006",
  volume =       "7",
  number =       "3",
  pages =        "890--898",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, En-route
                 caching, Robustness",
  DOI =          "doi:10.1016/j.asoc.2006.04.003",
  size =         "9 pages",
  abstract =     "Nowadays, large distributed databases are commonplace.
                 Client applications increasingly rely on accessing
                 objects from multiple remote hosts. The Internet itself
                 is a huge network of computers, sending documents
                 point-to-point by routing packeted data over multiple
                 intermediate relays. As hubs in the network become over
                 used, slowdowns and timeouts can disrupt the process.
                 It is thus worth to think about ways to minimise these
                 effects. Caching, i.e. storing replicas of
                 previously-seen objects for later reuse, has the
                 potential for generating large bandwidth savings and in
                 turn a significant decrease in response time. En-route
                 caching is the concept that all nodes in a network are
                 equipped with a cache, and may opt to keep copies of
                 some documents for future reuse [X. Tang, S.T. Chanson,
                 Coordinated en-route web caching, IEEE Transact.
                 Comput. 51 6 (2002) 595-607]. The rules used for such
                 decisions are called caching strategies. Designing such
                 strategies is a challenging task, because the different
                 nodes interact, resulting in a complex, dynamic system.
                 In this paper, we use genetic programming to evolve
                 good caching strategies, both for specific networks and
                 network classes. An important result is a new
                 innovative caching strategy that outperforms current
                 state-of-the-art methods.",
  notes =        "cites \cite{Paterson:1997:ecacGP},

                 Caching Internet documents. Caching as deletion
                 strategy. GP evolves priority (of cached www documents)
                 strategy removes low priority doc from cache until
                 space is available for new doc. Pop 60, 100 gens.
                 Network simulation on cluster of 6 linux workstations.
                 Linear network, GP almost optimal. Appealing GP
                 solution RUDF Priority = last time
                 accessed*(distance+access count).

                 p896 GPfinal+RUDF 'significantly outperformed all the
                 other strategies tested, with a slight advantage of
                 RUDF over GPfinal'.",

Genetic Programming entries for Jurgen Branke Pablo J Funes Frederik Thiele