Giggle: a framework for constructing scalable replica location services
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Replica creation strategy based on quantum evolutionary algorithm in data gird
Knowledge-Based Systems
| Hi-index | 0.00 |
Data Grid replication is critical for improving data intensive applications performance, providing fault tolerance and load balancing. Most of the techniques for data replication use Giggle as a framework for Replica Location Services (RLS), combined with other services for replica selection and optimization. Our previous work have proposed an enhanced Giggle framework, that simplify the location service using a flat catalogue structure, that combined with appropriate heuristic, obtain much better performances than traditional approaches. With this aim, we propose the use of Emergent Artificial Intelligence (EAI) techniques on data replication: Particle Swarm Optimisation(PSO) and Ant Colony Optimisation(ACO). This paper contribution is an experiment comparison between PSO, ACO, a canonical replication algorithm and other state of the art economic model replication algorithm. The experiments are design on two different network topologies. The simulation results confirm that PSO and ACO using the enhanced Giggle, improve performance over traditional solutions.