Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Evaluation of an Economy-Based File Replication Strategy for a Data Grid
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
A Decentralized, Adaptive Replica Location Mechanism
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Replica Placement Design with Static Optimality and Dynamic Maintainability
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Ant Algorithm for File Replica Selection in Data Grid
SKG '05 Proceedings of the First International Conference on Semantics, Knowledge and Grid
An on-line replication strategy to increase availability in Data Grids
Future Generation Computer Systems
Efficient Model for Replica Consistency Maintenance in Data Grids
CSA '08 Proceedings of the International Symposium on Computer Science and its Applications
Optimal replica placement in hierarchical Data Grids with locality assurance
Journal of Parallel and Distributed Computing
A Weight-Based Dynamic Replica Replacement Strategy in Data Grids
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Agent Based Replica Placement in a Data Grid Environement
CICSYN '09 Proceedings of the 2009 First International Conference on Computational Intelligence, Communication Systems and Networks
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
PSO-grid data replication service
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
A New Replica Creation and Placement Algorithm for Data Grid Environment
DSDE '10 Proceedings of the 2010 International Conference on Data Storage and Data Engineering
Emergent algorithms for replica location and selection in data grid
Future Generation Computer Systems
PSO vs. ACO, data grid replication services performance evaluation
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
A New Dynamic Replication Algorithm for Hierarchy Networks in Data Grid
3PGCIC '11 Proceedings of the 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
An LIRS-Based Replica Replacement Strategy for Data-Intensive Applications
TRUSTCOM '11 Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
A dynamic replica management strategy in data grid
Journal of Network and Computer Applications
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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As a research branch of grid computing, data grid focuses on the management of large-scale distributed data sets. Replica management is one of the most important issues in the data grid, which can offer fast data access time, high data availability and low bandwidth consumption. Computing Intelligent Algorithm (CIA) has been proved to be effective in the solution of large-scale distributed computing problems, whereas Quantum Evolutionary Algorithm (QEA) is one of these excellent optimization algorithms and little literatures are made for its application in Data Grid Replica Management (DGRM). This paper focuses on the application of the QEA in data grid replica creation strategy. A QEA-based global replica creation strategy is proposed after reviewing the replica creation strategies. The optimization model is divided into single and multi data replica creation two parts. The representation, evaluation and constraint procedure three key technologies problems for each part are discussed in detail. The detail algorithm of QEA based replica creation is provided. The experiments were carried out with OptorSim, and the results have shown that QEA-based replica creation strategy can effectively reduce the job response time and network bandwidth consumption, comparing to Genetic Algorithms (GAs), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) algorithms. Especially, its performance becomes better and better with the incensement of the number of jobs. The non-parametric statistical tests are used to verify the significant of QEA.