Exploiting Interest Locality for Peer-Assisted Search in UGC Video Systems

While there are several ways for video finding in UGC (user generated content) video systems, video search is still the number one source of video views in aggregation. In this paper, we propose to use peer-assisted search to alleviate the server burden caused by video search. To this end, we have measured and analyzed YouKu, the largest UGC video system in China. With a large dataset, we have found non-power law distribution of video popularity, low replication level for popular videos, skewed user activity and interest locality. Based on the findings, we design two-layer hierarchical semantic overlay structures to implement peer-assisted search for UGC video systems. A novel search algorithm called WISE is proposed to guide queries quickly to the semantically relevant clusters by visiting a very small fraction of nodes. Simulations using the YouKu trace demonstrate that WISE is effective and helpful to assist the search in UGC video systems. To the best of our knowledge, this is the first work to study peer-assisted search in UGC.

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