Applying the ant colony optimization algorithm to the spatial cluster scheduling problem

In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters. An important operation following this object clustering is to schedule the processing of the clusters such that the number of times that the same objects to be fetched into memory can be minimized. Proposed a cluster-sequencing method to minimize the I/O cost in spatial join processing. The key issue behind that method is how to produce a better sequence of clusters to guide the scheduling. This paper describes a new method that applies the ant colony optimization algorithm to produce better scheduling sequence. Preliminary experiments have been conducted and simulation results show that the scheduling sequence produced by the new method is much better than the original one in the sense that over 19% of the extra fetching time used for fetching those overlapping objects between spatial clusters can be saved.