A hybrid approach for cost-effective media streaming based on prediction of demand in community networks

Abstract

Media streaming applications have recently attracted large number of users in the Internet. This large demand creates a burden on existing infrastructure to sustain the QoS guarantees. The huge capacity resources and power consumption are the main aspects that hinder the evolution of media streaming applications in the Internet. This motivates us to explore new approaches in collaborative media streaming in community networks (e.g., college and office campuses) residing in a larger network of the access connection provider such as Internet Service Provider (ISP). The proposed approaches are aimed at exploiting the redundancy and abundantly available network "micro-resources" in a community network to create an aggregate virtual "macro-resource". Specifically, the ISP can leverage its control over a large amount of under-utilized micro-resources in a community network to replicate into caches of those micro-resources files that are desired in that community. In this paper, we characterize the evolution of demand in a community network. By using this demand-awareness model (i.e., future prediction of the demand in a community network), micro-resources in the community network can be optimally utilized. We show analytically and using simulations that this approach mitigates the cost of media streaming on both the Content Delivery Networks (CDNs) and ISPs. By allocating micro-resources to manage the demand in a community network on a per-need basis, much of the network and computing capacities at the edge-servers can be alleviated. This reduces the cost on the CDN in terms of both purchase and maintenance. Moreover, ISPs can reduce the bandwidth and power consumption in their networks by pushing the media content closer to users in the community network.