A prediction-based dynamic replication strategy for data-intensive applications

- Intelligent Replica Manager - a new strategy for prediction-based dynamic replication is proposed.
- Multi-criteria based replication algorithm is proposed for replica placement.
- Modified apriori algorithm is designed for frequent datasets prediction.
- Comparison with existing prediction-based dynamic replication methods.
- Comparison and testing of the proposed strategy with other strategies for different job size.

Data-intensive applications produce huge amount of data sets which need to be analyzed among geographically distributed nodes in grid computing environment. Data replication is essential in this environment to reduce the data access latency and to improve the data availability across several grid sites. In this work, an Intelligent Replica Manager (IRM) is designed and incorporated in the middleware of the grid for scheduling data-intensive applications. IRM uses a Multi-criteria based replication algorithm which considers multiple parameters like storage capacity, bandwidth and communication cost of the neighboring sites before taking decisions for the selection and placement of replica. Additionally, future needs of the grid site are predicted in advance using modified apriori algorithm, which is an association rule based mining technique. This IRM based strategy reduces the data availability time, data access time and make span. The simulation results prove that the proposed strategy outperforms the existing strategies.