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
Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
SIAM Journal on 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
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
Planning spatial workflows to optimize grid performance
Proceedings of the 2006 ACM symposium on Applied computing
Job scheduling and data replication on data grids
Future Generation Computer Systems
Load distribution of analytical query workloads for database cluster architectures
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Adaptive hierarchical scheduling policy for enterprise grid computing systems
Journal of Network and Computer Applications
Scalable and distributed mechanisms for integrated scheduling and replication in data grids
ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
Efficient data consolidation in grid networks and performance analysis
Future Generation Computer Systems
DECO: data replication and execution CO-scheduling for utility grids
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
Study of scheduling strategies in a dynamic data grid environment
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Replica-Aware job scheduling in distributed systems
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Simultaneous scheduling of replication and computation for bioinformatic applications on the grid
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Design and analysis of data management in scalable parallel scripting
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Improving job scheduling performance with dynamic replication strategy in data grids
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
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 Such problems involve loosely coupled jobs and large data sets distributed remotely Data Grids have found applications in scientific research fields of high-energy physics, life sciences etc as well as in the enterprises 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, scalability, 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 The paper deals with the problem of integrating the scheduling and replication strategies As part of the solution, we have proposed an Integrated Replication and Scheduling Strategy (IRS) which aims at an iterative improvement of the performance based on the coupling between the scheduling and replication strategies Results suggest that, in the context of our experiments, IRS performs better than several well-known replication strategies.