Dynamic Replica Selection Using Improved Kernel Density Estimation

Replication service in Distributed Systems can reduce access latency and bandwidth consumption. When different nodes hold replicas accessed, there will be a significant benefit by selecting the best replica. Most of the existed replication strategies deal with the prediction of the response time. However, these strategies do not take fully into account the network dynamic and access locality. To solve this problem, a dynamic replica selection strategy using improved Kernel Density Estimation (KDE) is presented. Firstly, it distinguishes old replicas from new ones. Then, it predicts the network load and available bandwidth to choose the best replica. The improved KDE can select accurately the best accessed replica with only a little history data, which is very useful in a dynamic network. Simulation results demonstrate the efficiency and effectiveness of improved KDE in comparison with other approaches.