PACS: Chopping and shuffling large contents for faster opportunistic dissemination

A common assumption in intermittently-connected (or opportunistic) mobile networks is that any contact has enough capacity to transfer the required amount of data. Although such an assumption is reasonable for analytical purposes and when contents are small, it does not hold anymore when nodes produce contents that are larger than the capacity of a contact. In such a case, nodes must slice data and send fragments separately, which allows better use of short contacts and progressive dissemination of large contents data pieces. The question here is to design the best strategy for deciding which piece(s) to transmit whenever nodes meet. In this paper, we present the design and evaluation of PACS (Prevalence-Aware Content Spreading), a completely distributed algorithm that selects pieces to transfer based on their popularity. We evaluate the performance of PACS using both synthetic and real traces from intermittently-connected networks. When compared with sequential and randomized solutions, we show that PACS significantly outperforms these approaches both in terms of latency to achieve full dissemination and ratio of effective contacts. Moreover, PACS achieves performance levels that are extremely close to a centralized version based on an oracle.