The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Data management and transfer in high-performance computational grid environments
Parallel Computing - Parallel data-intensive algorithms and applications
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
A taxonomy of Data Grids for distributed data sharing, management, and processing
ACM Computing Surveys (CSUR)
Optimal Placement of Replicas in Data Grid Environments with Locality Assurance
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Dynamic replication algorithms for the multi-tier Data Grid
Future Generation Computer Systems - Special issue: Parallel computing technologies
A dynamic replica management strategy in data grid
Journal of Network and Computer Applications
The Journal of Supercomputing
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Data grids support access to widely distributed storage for large numbers of users accessing potentially many large files. Efficient access is hindered by the high latency of the Internet. To improve access time, replication at nearby sites may be used. Replication also provides high availability, decreased bandwidth use, enhanced fault tolerance, and improved scalability. Resource availability, network latency, and user requests in a grid environment may vary with time. Any replica placement strategy must be able to adapt to such dynamic behavior. In this paper, we describe a new dynamic replica placement algorithm, Popularity Based Replica Placement (PBRP), for hierarchical data grids which is guided by file "popularity". Our goal is to place replicas close to clients to reduce data access time while still using network and storage resources efficiently. The effectiveness of PBRP depends on the selection of a threshold value related to file popularity. We also present Adaptive-PBRP (APBRP) that determines this threshold dynamically based on data request arrival rates. We evaluate both algorithms using simulation. Results for a range of data access patterns show that our algorithms can shorten job execution time significantly and reduce bandwidth consumption compared to other dynamic replication methods.