Technical Report Number
Graph search has been employed by many AI techniques and applications. A natural way to improve the eﬃciency of search is to utilize ad- vanced, more powerful computing platforms. However, expensive computing infrastructures, such as supercomputers and large-scale clusters, are traditionally available to only a limited number of projects and researchers. As a results, most AI applications, with access to only commodity com- puters and clusters, cannot beneﬁt from the eﬃciency improvements of high-performance parallel search algorithms. Cloud computing provides an attractive, highly accessible alternative to other traditional high- performance computing platforms. In this paper, we first show that the run-time of our stochastic search algorithm in planning is a heavy-tailed distribution, which has a remarkable variability. Second, we propose an algorithm framework that takes advantage of cloud computing.
Lu, Qiang; Xu, You; Huang, Ruoyun; and Chen, Yixin, "Cloud Computing for Scalable Planning by Stochastic Search" Report Number: WUCSE-2010-29 (2010). All Computer Science and Engineering Research.
Permanent URL: http://dx.doi.org/10.7936/K7JD4V06