Sequential and parallel ant colony strategies for cluster scheduling in spatial databases

Authors:
Jitian Xiao;Huaizhong Li
Affiliations:
School of Computer and Information Science, Edith Cowan University, Mount Lawley, WA, Australia;School of Computer and Information Science, Edith Cowan University, Mount Lawley, WA, Australia
Venue:
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
Year:
2004

Citing 4
Cited 0

Spatial data models and query processing

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Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters and then to schedule the processing of the clusters such that the number of times the same objects to be fetched into memory can be minimized. The key issue of cluster scheduling is how to produce a better sequence of clusters to guide the scheduling. This paper describes strategies that apply the ant colony optimization (ACO) algorithm to produce cluster scheduling sequence. Since the structure of the ACO is highly suitable for parallelization, parallel algorithms are also developed to improve the performance of the algorithms. We evaluated and illustrated that that the scheduling sequence produced by the new method is much better than existing approaches.