Improving Data Availability through Dynamic Model-Driven Replication in Large Peer-to-Peer Communities

Efficient data sharing in global peer-to-peer systems is complicated by erratic node failure, unreliable network connectivity and limited bandwidth. Replicating data on multiple nodes can improve availability and response time. Yet determining when and where to replicate data in order to meet performance goals in large-scale systems with many users and files, dynamic network characteristics, and changing user behavior is difficult. We propose an approach in which peers create replicas automatically in a decentralized fashion, as required to meet availability goals. The aim of our framework is to maintain a threshold level of availability at all times. We identify a set of factors that hinder data availability and propose a model that decides when more replication is necessary. We evaluate the accuracy and performance of the proposed model using simulations. Our preliminary results show that the model is effective in predicting the required number of replicas in the system.