Dynamic Replica Selection Services Based on State Evaluation Strategy

Data Replication in large scale distributed systems was introduced for enhancing data availability, reducing network flow and access latency. Efficient selecting the best replica to minimize access latency remains a fundamental challenge. Existing algorithms mostly use the previous history of replica transfers stored locally to estimate the replica access time for selection replica. It will not always give the best result especially stale access replica log. In this research, we propose a replica selection services according to evaluate replication state. To selected the best replica from replication state information according to replica node state fuzzification evaluation. The replica node state’s duration time called “statelife-cycle”. It is calculated by grey prediction model. At last,the experimental results show that proposed replica selection services can improve performance to reducing access latency.