Minimizing job execution time in Data Grid by A fuzzy dynamic replication algorithm

The nature of Data Grids is dynamic. In these systems, data access patterns of users and network latency may change. The system needs to meet data availability reliability. Data replication is a wellknown method for improving performance parameters such as data access time, availability, load balancing, and reliability. Here, a novel dynamic algorithm is proposed that uses fuzzy inference systems to manage replication for increasing performance. The proposed algorithm uses a new comprehensive set of decision parameters and fuzzy logic in each phase to reduce the inefficiency caused by wrong decisions in different phases in a practical Grid. The algorithm uses two fuzzy interfere systems to select an appropriate place for new replication and a less valuable file for deleting when storage space is full. It places the new replica in a suitable site where the file could possibly be needed soon with high probability. It also prevents deleting valuable files using a fuzzy valuation function. The algorithm was simulated by the OptorSim simulator. The results demonstrated that the algorithm is more effective than other replication methods in terms of the number of created replications, the percentage of storage used, and the job execution time.