On the Potential of Recommendation Technologies for Efficient Content Delivery Networks
During the last decade, we have witnessed a substantial change in content delivery networks (CDNs) and user access paradigms. If previously, users consumed content from a central server through their personal computers, nowadays they can reach a wide variety of repositories from virtually everywhere using mobile devices. This results in a considerable time-, location-, and event-based volatility of content popularity. In such a context, it is imperative for CDNs to put in place adaptive content management strategies, thus, improving the quality of services provided to users and decreasing the costs. In this paper, we introduce predictive content distribution strategies inspired by methods developed in the Recommender Systems area. Specifically, we outline different content placement strategies based on the observed user consumption patterns, and advocate their applicability in the state of the art CDNs.
recommendation technologies cdn personalization content placement algorithms performance in-network learning