Paper Info
Title
Sequential and parallel ant colony strategies for cluster scheduling in spatial databases
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.
Year
DOI
Venue
2004
10.1007/978-3-540-30566-8_77
ISPA
Keywords
Field
DocType
scheduling sequence,ant colony optimization,spatial object,spatial databases,parallel ant colony strategy,cluster scheduling,o cost,new method,cluster scheduling sequence,key issue,common method,better sequence,parallel algorithm,ant colony,spatial database
Ant colony optimization algorithms,Fair-share scheduling,Parallel algorithm,Computer science,Scheduling (computing),Parallel computing,Swarm intelligence,Two-level scheduling,Ant colony,Dynamic priority scheduling,Distributed computing
Conference
Volume
ISSN
ISBN
3358
0302-9743
3-540-24128-0
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Jitian Xiao1258.27
Huaizhong Li217718.16