OnRipple: A Distributed Overlay Framework for Targeted Immunization in Large-Scale Networks

Targeted immunization has been studied as an applicable measure for network epidemics. Unless global information is collected, existing targeted strategies are short of strength in organizing a potent structure to spread the vaccines fleetly. We propose a novel dynamic immunization mechanism, called OnRipple, to propagate vaccines in susceptible-infected-removed (SIR) epidemiological model. With a light-weighted, scalable and distributed framework and an "axis" structure, OnRipple builds-up an effective overlay network. Thus, nodes have essential sampling information of the whole network and targeted immunization can be easily implemented. Nodes are also able to radiate vaccines to both nearby and distant participants almost at the same time. We analyze the performance of this strategy by simulations on small-world networks. On average, OnRipple is 34 % faster than traditional targeted immunization. It is proven to be flexible and effective in large-scale peer-to-peer (P2P) networks and scale-free networks.