Abstract

In this paper we investigate the performance of distributed heuristic search methods based on a well-known heuristic search algorithm, the iterative deepening A^* (IDA^*). The contribution of this paper includes proposing and assessing a distributed algorithm for IDA^*. The assessment is based on space, time and solution quality that are quantified in terms of several performance parameters such as generated search space and real execution time among others. The experiments are conducted on a cluster computer system consisting of 16 hosts built around a general-purpose network. The objective of this research is to investigate the feasibility of cluster computing as an alternative for hosting applications requiring intensive graph search. The results reveal that cluster computing improves on the performance of IDA^* at a reasonable cost.

Keywords: Heuristic search; Parallel and distributed computing; IDA^*; Performance evaluation

Article Outline

1. Introduction

2. Previous work

3. Cluster-based formulation of IDA^*

4. A framework for performance assessment

5. Experimental results

6. Conclusions and further work

Acknowledgements

References

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