Coalition game for video content clustering in content delivery networks

Game theory is a powerful tool that has recently been used in networks to improve the end users' quality of experience (e.g. decreased response time, higher delivery rate). In this paper, we propose to use game theory in the context of Content Delivery Networks (CDNs) to organize video contents into clusters having similar request profiles. The popularity of each content in the cluster can be determined from the popularity of the representative of the cluster and used to store the most popular contents close to end users. A group of experts and a decision-maker predict the popularity of the representative of the cluster. This considerably reduces the number of experts used. More precisely, we model the clustering problem as a hedonic coalition formation game where each coalition represents a cluster. The coalition game converges to a stable partition representing a solution of the problem considered. We compare the results of this approach with the clustering obtained by the K-means algorithm. We evaluate the impact of the content profile observation window considered to establish the clustering. We also evaluate the complexity of the proposed algorithm. Simulation results are obtained on traces of a real CDN. Finally, we extend the proposed approach to model an on-line clustering reflecting the CDN dynamics in terms of proposed contents and contents solicitations.