Online microblogging services, as exemplified by Twitter [9] and Yammer [12], have become immensely popular during the latest three years. Twitter, the most successful microblogging service, has attracted more than 41.7 million users as of July 2009 [25] and is still growing fast. However, current microblogging systems severely suffer from performance bottlenecks and central points of failure due to their centralized architecture. Thus, centralized microblogging systems may threaten the scalability, reliability, as well as availability of the offered services, not to mention the extremely high operational and maintenance cost. However, it is not trivial to decentralize microblogging services in a peer-to-peer fashion. The challenges first derive from the heterogeneity of the inherent online social network

Recent studies show that network coding improves multicast session throughput. In this paper, we demonstrate how random linear network coding can be incorporated to provide network diagnosis for peer-to-peer systems. We present a new trace collection protocol that allows operators to diagnose peer-topeer networks. It is essential to monitor large-scale peer-to-peer applications by collecting measurements referred to as snapshots from the peers. However, existing solutions are not scalable and fail to collect measurements from peers that departed before the time of collection. We use progressive random linear network coding to disseminate the snapshots in the network, from which the server pulls data in a delayed fashion. We leverage the power of progressive encoding to increase block diversity and tolerate extreme block losses by introducing redundancy in the network. Peers cooperate by allocating cache capacity for other peers. Snapshots of departed peers can thus be retrieved from the network. We show how our protocol controls the redundancy introduced through progressive encoding and thus scales to large number of peers and tolerates high level of peer dynamics.