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Kernel Based Mechanisms for High Performance I/O

P2P networks are becoming increasingly used for wide-scale collaborative information spreading over the Internet. Thus, the ability to share information with large group of network nodes at near-optimal cost may be the one step that will allow P2P networks to replace traditional broadcast. For large groups, there are substantial inefficiencies that result from using deterministic tree-based approaches to share information with many recipients. As P2P networks increase in size, communication protocols must be designed to cope with poor reliability and with the dynamism of the underlying network. To this end, in this thesis, we explore one promising solution for collaborative information spreading, called randomized gossip. Gossip techniques for information dissemination are central in numerous distributed systems, and have been proven to spread information without centralized control, with remarkable speed and inherent fault tolerance. Building on this methodology, we introduce and formally analyze two sets of randomized gossip protocols for collaborative information spreading in P2P networks. First, we study randomized gossip protocols in the context of collaborative data delivery. In this context, multiple fragments of data reside at different nodes, and the challenge is to simultaneously deliver all fragments to all nodes. We analyze the intrinsic relations between the gossip of multiple, simultaneous messages. We provide an efficient solution that exhibits the inherent robustness and scalability of gossip. Moreover, our approach benefits from the simplicity of gossip and has low message connections and computation overhead.