Computer Networks and ISDN Systems - Selected papers of the 3rd international caching workshop
The AppLeS parameter sweep template: user-level middleware for the grid
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Gathering at the well: creating communities for grid I/O
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Simulation of Dynamic Grid Replication Strategies in OptorSim
GRID '02 Proceedings of the Third 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
The Globus Project: A Status Report
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
A Unified Resource Scheduling Framework for Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms
IEEE Transactions on Parallel and Distributed Systems
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Study of scheduling strategies in a dynamic data grid environment
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Integration of scheduling and replication in data grids
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
Computers and Operations Research
Hopfield neural network for simultaneous job scheduling and data replication in grids
Future Generation Computer Systems
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Data Grids seek to harness geographically distributed resources for large-scale data-intensive problems. The issues that need to be considered in the Data Grid research area include resource management for computation and data. Computation management comprises scheduling of jobs, load balancing, fault tolerance and response time; while data management includes replication and movement of data at selected sites. As jobs are data intensive, data management issues often become integral to the problems of scheduling and effective resource management in the Data Grids. Therefore, integration of data replication and scheduling strategies is important. Such an integrating solution is either non-existent or work in a centralized manner which is not scalable. The paper deals with the problem of integrating the scheduling and replication strategies in a distributed manner. As part of the solution, we have proposed a Distributed Replication and Scheduling Strategy (DistReSS) which aims at an iterative improvement of the performance based on coupling between scheduling and replication, which is achieved in distributed and hierarchical fashion. Results suggest that, in the context of our experiments, DistReSS performs comparable to the centralized approach when the parameters are tuned properly in addition to being more scalable to the centralized approach.