Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Future Generation Computer Systems - Special issue on metacomputing
Journal of Parallel and Distributed Computing
MPI-The Complete Reference, Volume 1: The MPI Core
MPI-The Complete Reference, Volume 1: The MPI Core
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
A Directory Service for Configuring High-Performance Distributed Computations
HPDC '97 Proceedings of the 6th 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 Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Security-Driven Heuristics and A Fast Genetic Algorithm for Trusted Grid Job Scheduling
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Quality of Service on the Grid Via Metascheduling with Resource Co-Scheduling and Co-Reservation
International Journal of High Performance Computing Applications
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
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Grid systems, constituted by multisite andmulti-owner timeshared resources, make a great amount of locally unemployed computational power accessible to users. To profitably exploit this power for processing computationally intensive grid applications, an efficient multisite mapping must be conceived. The mapping of cooperating and communicating application subtasks, already known as NP-complete for parallel systems, results even harder in grid computing because the availability and workload of grid resources change dynamically, so evolutionary techniques can be adopted to find near-optimal solutions. In this paper a mapping tool based on a multiobjective Differential Evolution algorithm is presented. The aim is to reduce the execution time of the application by selecting among all the potential solutions the one which minimizes the degree of use of the grid resources and, at the same time, complies with Quality of Service requirements. The proposed mapper is assessed on some artificial problems differing in application sizes and workload constraints.