Concurrency and locality of content demand

Distribution of media data over the Internet is increasing in popularity and volume. This poses challenges not only for network operators but also for service providers when it comes to serving the demand in a cost-efficient way. In this paper, we approach this problem by investigating the potential of co-operative approaches where locality in space (users in the same network) and locality in time (concurrent downloads) are exploited such that as many requests as possible may be handled inside the access and metro networks. This approach may contribute not only to reducing transport costs (less traffic in core networks and at peering points) by but also improve the end user experience (by reduced round trip times and exclusion of some possible bottlenecks). To this end we develop a method to measure the possible gains from, firstly, optimal handling of concurrent downloads and, secondly, optimal utilization local availability. We apply the method to BitTorrent data from two metropolitan access networks and find that the bandwidth savings amount to between 10% and 20% when optimizing concurrent downloads and between 56% and 66% when exploiting local availability with a simulated network cache.