An Optimization of Backup Storage using Backup History and Cache Knowledge in reducing Data Fragmentation for In_line deduplication in Distributed

The chunks of data that are generated after the backup are physically distributed after deduplication in backup system, which creates a problem know as fragmentation. Basically fragmentation basically comes into sparse and outof-order containers. The sparse container adversely affect the performance while restoring the database and garbage collection effectively , while the out-of-order container brings an adverse effect on the performance issue if the restore cache built is small. To overcome this fragmentation problem , we propose a method of History-Aware Rewriting algorithm (HAR) and also Cache-Aware Filter (CAF).HAR will gather the historical information in backup systems to define, identify and reduce sparse containers, and CAF acknowledges restore cache knowledge to find the out-of-order containers that impacts restore performance. CAF supports HAR in datasets where out-of-order containers are prominent. To get rid of metadata of the garbage collection, we exploit Container-Marker Algorithm (CMA) to gather valid containers instead of valid chunks. My output helps to prove how HAR significantly improves the restore