Selfishness-Aware Data-Driven Overlay Network

Data-driven overlay network (DONet) especially works well with live-event streaming because data can be propagated in a relatively continuous way even with node dynamics. However, optimal streaming demands the cooperation of individual nodes. In the real world, some selfish participants which might delay forwarding or stop forwarding data can affect the overall streaming quality. To address the selfishness issue, we propose a selfishness-aware DONet (SA-DONet) in this paper. SA-DONet allows each node associative with an altruism value for its contributions to peers. Based on the altruism value, segment requesting and sending algorithms are designed to ensure the more altruistic nodes will have more chances to be served. The primary characteristic of our mechanism lies in three aspects. Firstly, SA-DONet can discover the selfish nodes in a decentralized manner and adjust the segment sending and requesting strategy dynamically. Secondly, selfish assessment (altruism value) comes from the node´s history and doesn´t require any extra probe and measuring packets. Lastly, our algorithms remain comparable computing complexity to DONet. Simulation results show that compared with DONet, even with a significant portion of nodes being selfish, SA-DONet can improve the streaming quality of global multicast session with low control overhead.