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We focus in this report on two main axes. The first is dedicated to the studyof the effect of replicas distribution on data grid performances. In thisrespect, our main contributions are as follows: 1) An overview of replicationstrategies mainly from the viewpoints of the considered parameters in theirassociated steps as well as the used metrics in the literature for theirevaluation. 2) A study of the impact of placement strategies on data gridperformance which motivated the analysis of the effect of the replicasdistribution quality on the performance results of replication strategies. 3)The proposal of new evaluation metrics dedicated to the evaluation of thedistribution quality. 4) The setting of an objective evaluation of replicationstrategies which is based on a beforehand assessment of the distributionquality.  The second axis is mainly dedicated to exploiting results of data miningtechniques to enhance performances of replication strategies. With respect tothis axis, we mainly concentrate on the following contributions listed below:1) The study of the strengths and the drawbacks of the main replicationstrategies based on data mining techniques and how these latter are applied inthis context. 2) The proposal of a new guideline to data mining application inthe context of data grid replication strategies. 3) The proposal of a newalgorithm for mining maximal frequent correlated patterns. The input of thisalgorithm is obtained through a preliminary step focusing on how to adapt therequired grid concepts to the data mining algorithm. 4) The design and theimplementation of a new replication strategy based on a data mining technique,and more precisely correlated patterns.

Replication in Data Grids: Metrics and Strategies