A Critical Survey of Data Grid Replication Strategies Based on Data Mining Techniques

Abstract

Abstract Replication is one common way to effectively address challenges for improving the data management in data grids. It has attracted a lot of work and many replication strategies have therefore been proposed. Most of these strategies consider a single file-based granularity and do not take into account file access patterns or possible file correlations. However, file correlations become an increasingly important consideration for performance enhancement in data grids. In this regard, the knowledge about file correlations can be extracted from historical and operational data using the techniques of the data mining field. Data mining techniques have proved to offer a powerful tool facilitating the extraction of meaningful knowledge from large data sets. As a consequence of the convergence of data mining and data grid, mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance data management in data grids. More precisely, in this paper, the extracted knowledge is used to enhance replica management. Gaps in the current literature and opportunities for further research are presented. In addition, we propose a new guideline to data mining application in the context of data grid replication strategies. To the best of our knowledge, this is the first survey mainly dedicated to data grid replication strategies based on data mining techniques.