Load-balanced migration of social media to content clouds

Social networked applications have been more and more popular, and have brought great challenges to the network engineering, particularly the huge demands of bandwidth and storage for social media. The recently emerged content clouds shed light on this dilemma. Towards the migration to clouds, partitioning the social contents has drawn significant interests from the literature. Yet the existing works focus on preserving the social relationship only, while an important factor, user access pattern, is largely overlooked. In this paper, by examining a large collection of YouTube video data, we first demonstrate that partitioning the network entirely based on social relationship would lead to unbalanced partitions in terms of access. We further analyze the role of social relationship in the social media applications, and conclude that user access pattern should be taken into account and social relationship should be dynamically preserved. We formulate the problem as a constrained k-medoids clustering problem, and propose a novel Weighted Partitioning Around Medoids (wPAM) solution. We present a dissimilarity/similarity metric to facilitate the preservation of the social relationship. We compare our solution with other state-of-the-art algorithms, and the preliminary results show that it significantly decreases the access deviation in each cloud server, and flexibly preserves the social relationship.