Dynamic replication aware load blanced scheduling in distributed environment / Said Bakhshad - Said Bakhshad, Bakhshad (2018)
Grid computing is an effective distributed and adaptable processing network that manages a huge number of data applications. Proficient usage of existing resources in distributed grid network is still of great demand today. Additionally, it is of more crucial demand in a very dynamic dispersed surrounding such as grid. The grid processing is a viable computing surrounding. Data replication is viewed as a vital boost mechanism in data grids. The storage space limitations of traditional distributed systems can be overcome, to completely point out the resources of computing sites of under-utilized computing resources in the distributed environments. However, the scope state of-the-art replication procedures ignore the replica locations during jobs scheduling. It assigns the request of the node and then the node’s Replication Manager searches the presence of the replica. Several algorithms have been proposed and studied for scheduling and data replication, however a little research has been done so far on capturing and minimizing the migration rate of data from an existing available replica site to a next site on the basis of data scheduling in order to minimize the transfers and deletion rate. In this regard, Modified Dynamic Hierarchical Replication (MDHR) is one of the recent and important effort toward this issue. MDHR is dependent on the last request of the data replica, size of the replica, no. of accesses, and it chooses the outstanding replica from the replica list based on turnaround time or response time, the access latency, demand waiting in queue for execution, the grid sites distance and CPU capability of processing. But it did not consider replica location at the time of scheduling which led to increase in the execution time and data migration rate as well. In this manner, scheduling is critical, which causes the assigining of job to site with replica. In this case, if jobs are not scheduled properly at particular points, the processing resources will be squandered. We propose a novel dynamic Replication Aware Load Balanced Scheduling (DRALBS) algorithm, that considers the replica location dynamically at the time of the scheduling of the job. The simulation of the proposed algorithm shows promising results and better performance compared to the current state-of-the-art (MDHR) algorithm. The response and average access time has been significantly decreased, thus reducing the overall mean job execution time data migration and deletion rate as well as bandwidth consumption.