Assessing speed-ups in commodity cloud storage services for distributed evolutionary algorithms

Cloud computing is lately becoming a part of the tool-set that the scientist uses to perform compute-intensive tasks. In particular, cloud storage is an easy and convenient way of storing files that will be accessible over the Internet, but can also be used for distributing those files for performing computation on them. In this paper we describe how such a service commercialized by Dropbox is used for pool-based evolutionary algorithms. A prototype system is described and its performance measured over deceptive combinatorial optimization problems using two different substrates: WiFi and wired, finding that, for some type of problems and using commodity hardware, cloud storage systems can profitably be used as a platform for distributed evolutionary algorithms; however, performance is influenced by the type of underlying network. After introducing the method in a previous paper, in this paper we focus on measuring this influence, finding that wired is faster than WiFi for any number of nodes. We have also performed an experiment with a few more computers to see whether speedup keeps up with the number of nodes.