- 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},
\cite{oneill:1999:AGCA},
\cite{Branke:EEC:gecco2004}.
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'.",
