PROB: A lightweight approach for fast content propagation in P2P networks

Nowadays, content sharing using P2P networks is one of the most important Internet applications. As these networks gain more popularity, boosting the propagation of newly-uploaded objects becomes critically important. Rapid propagation would enable networks to protect object availability against seed departure, and make themselves the ideal distribution platform for user-generated content. Through real experience, we find that even adopting a simple word-of-mouth exchange can enhance content spread, especially by exposing users to diverse content and speeding up the propagation of objects with high potential demand. To further exploit such benefits, a more intelligent exchange approach is needed to balance propagation speed and diversity through the ever-growing amount of objects. Hence, we propose a lightweight peer selection algorithm, PROB (PROportional random Browse). PROB adopts a model-driven approach to optimize the expected downloading throughput while maintaining diversity. Moreover, it achieves the balance in a stateless and probabilistic manner, thus reducing complexity and enabling practical deployment. Our trace based simulation and real deployment demonstrate that PROB can significantly speedup content spread compared with random/deterministic approaches.