Cloud Computing for Scalable Planning by Stochastic Search

Authors

Qiang Lu, Washington University in St Louis
You Xu, Washington University in St Louis
Ruoyun Huang, Washington University in St Louis
Yixin Chen, Washington University in St. LouisFollow

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

2010

Filename

WUCSE-2010-29.pdf

Technical Report Number

WUCSE-2010-29

Abstract

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.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7JD4V06

Recommended Citation

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.
https://openscholarship.wustl.edu/cse_research/43