TY - GEN
T1 - Replicating multi-quality web applications using ACO and bipartite graphs
AU - Mayer, Christopher B.
AU - Dressler, Judson
AU - Harlow, Felicia
AU - Brault, Gregory
AU - Candan, Kasim
PY - 2006
Y1 - 2006
N2 - This paper presents the application of the Ant Colony Optimization (ACO) meta-heuristic to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied at the desired quality level and replica update loads are minimized. Our ACO algorithm, AntDA, for solving the ASP's replication problem has several novel or infrequently seen features: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Testing shows that the best results are achieved by using pheromone and heuristics to traverse the bipartite graph in both directions. Additionally, experiments show that AntDA outperforms several other solution methods.
AB - This paper presents the application of the Ant Colony Optimization (ACO) meta-heuristic to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied at the desired quality level and replica update loads are minimized. Our ACO algorithm, AntDA, for solving the ASP's replication problem has several novel or infrequently seen features: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Testing shows that the best results are achieved by using pheromone and heuristics to traverse the bipartite graph in both directions. Additionally, experiments show that AntDA outperforms several other solution methods.
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U2 - 10.1007/11839088_24
DO - 10.1007/11839088_24
M3 - Conference contribution
AN - SCOPUS:33751364066
SN - 3540384820
SN - 9783540384823
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 270
EP - 281
BT - Ant Colony Optimization and Swarm Intelligence - 5th International Workshop, ANTS 2006, Proceedings
PB - Springer Verlag
T2 - Ant Colony Optimization and Swarm Intelligence - 5th International Workshop, ANTS 2006, Proceedings
Y2 - 4 September 2006 through 7 September 2006
ER -