%0 Generic %1 arXiv:cs/0701115v1 %A Merelo, J. J. %A Mora-Garcia, Antonio %A Laredo, J. L. J. %A Lupion, Juan %A Tricas, Fernando %D 2007 %K Cluster Computing Computing, Distributed, Evolutionary Neural Parallel, algorithms, and genetic %T Browser-based distributed evolutionary computation: performance and scaling behavior %U http://arxiv.org/pdf/cs/0701115 %X The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and interconnections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straightforward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behaviour when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due to the characteristics of the server. Several solutions for that problem are proposed.

@misc{arXiv:cs/0701115v1, abstract = {The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and interconnections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straightforward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behaviour when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due to the characteristics of the server. Several solutions for that problem are proposed.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Merelo, J. J. and Mora-Garcia, Antonio and Laredo, J. L. J. and Lupion, Juan and Tricas, Fernando}, biburl = {https://www.bibsonomy.org/bibtex/2f95d218ae3117bd97970082642737e49/brazovayeye}, howpublished = {arXiv:cs/0701115v1}, interhash = {31e6296e9876cf44a480beb874fc03b0}, intrahash = {f95d218ae3117bd97970082642737e49}, keywords = {Cluster Computing Computing, Distributed, Evolutionary Neural Parallel, algorithms, and genetic}, month = {18 January}, notes = {not a GP cf genetic_programming@yahoogroups.com Fri, 25 May 2007 07:29:02 -0000}, size = {8 pages}, timestamp = {2008-06-19T17:46:53.000+0200}, title = {Browser-based distributed evolutionary computation: performance and scaling behavior}, url = {http://arxiv.org/pdf/cs/0701115}, year = 2007 }