Computer
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Future Generation Computer Systems - Special issue on metacomputing
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
MySRB & SRB: Components of a Data Grid
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Grid Information Services for Distributed Resource Sharing
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
A Comparison among Grid Scheduling Algorithms for Independent Coarse-Grained Tasks
SAINT-W '04 Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
A Parallel Tabu Search Approach Based on Genetic Crossover Operation
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Intelligent Scheduling and Replication in Datagrids: a Synergistic Approach
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Agent-Based Resource Discovery and Selection for Dynamic Grids
WETICE '06 Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Improvement of Data Grid's Performance by Combining Job Scheduling with Dynamic Replication Strategy
GCC '07 Proceedings of the Sixth International Conference on Grid and Cooperative Computing
Journal of Parallel and Distributed Computing
Impact of Parallel Download on Job Scheduling in Data Grid Environment
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
Performance enhancement through hybrid replication and Genetic Algorithm co-scheduling in data grids
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-objective Problems
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 02
A parallel genetic algorithm in multi-objective optimization
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Parallelization methods for implementation of discharge simulation along resin insulator surfaces
Computers and Electrical Engineering
IEEE Transactions on Computers
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Grid computing is the combination of computer resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems: selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.