Parallel Computing
Design and implementations of Ninf: towards a global computing infrastructure
Future Generation Computer Systems - Special issue on metacomputing
SCILAB to SCILAB//: the Ouragan project
Parallel Computing - Clusters and computational grids for scientific computing
Design Issues of Network Enabled Server Systems for the Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
Dynamic Performance Forcasting for Network-Enabled Servers in a Metacomputing Environment
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Stork: Making Data Placement a First Class Citizen in the Grid
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Scalability in a GRID Server Discovery Mechanism
FTDCS '04 Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems
Data Management in Grid Applications Providers
DFMA '05 Proceedings of the First International Conference on Distributed Frameworks for Multimedia Applications
Two implementations of the preconditioned conjugate gradient method on heterogeneous computing grids
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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The GridRPC model [17] is an emerging standard promoted by the Global Grid Forum (GGF) that defines how to perform remote client-server computations on a distributed architecture. In this model data are sent back to the client at the end of every computation. This implies unnecessary communications when computed data are needed by an other server in further computations. Since, communication time is sometimes the dominant cost of remote computations, this cost has to be lowered. Several tools instantiate the GridRPC model such as NetSolve developed at the University of Tennessee, Knoxville, USA, and DIET developed at LIP laboratory, ENS Lyon, France. They are usually called Network Enabled Servers (NES). In this paper, we present a discussion of the data management solutions chosen for these two NES (NetSolve and DIET) as well as experimental results.