PAC'nPost : a peer-to-peer micro-blogging social network

In this thesis we provide a framework for a micro-blogging social network in an unstructured peer-topeer network. At a user level, a micro-blogging service provides (i) a means for publishing a microblog, (ii) a means to follow a micro-blogger, and (iii) a means to search for micro-blogs containing keywords. Since unstructured peer-to-peer networks do not bind either the data or the location of data to the nodes in the network, search in an unstructured network is necessarily probabilistic. Using the probably approximately correct (PAC) search architecture, the search of an unstructured network is based on a probabilistic search that queries a fixed number of nodes. We provide a mechanism by which information whose creation rate is high, such as micro-blogs, can be disseminated in the network in a rapid-yet-restrained manner, in order to be retrieved with high probability. We subject our framework to spikes in the data creation rate, as is common in micro-blogging social networks, and to various types of churn. Since both dissemination and retrieval incur bandwidth costs, we investigate the optimal replication of data, in the sense of minimizing the overall system bandwidth. We explore whether replicating the micro-blog posts of users with a larger number of followers more than the posts of other users can reduce the overall system bandwidth. Finally, we investigate trending keywords in our framework. Trending keywords are important in a micro-blogging social network as they provide users with breaking news they might not get from the users they follow. Whereas identifying trending keywords in a centrally managed system is relatively straightforward, in a distributed decentralized system, the nodes do not have access to the global statistics such as the frequency of the keywords and the information creation rate. We describe a two-step algorithm which is capable of detecting multiple trending keywords with moderate increase in bandwidth.