Telecom operators are starting the deployment of Content Delivery Networks (CDN) to better control and manage video contents injected into the network. Cache nodes placed close to end users can manage contents and adapt them to users' devices, while reducing video traffic in the core. By adopting the standardized MPEG-DASH technique, video contents can be delivered over HTTP. Thus, HTTP servers can be used to serve contents, while packagers running as software can prepare live contents. This paves the way for virtualizing the CDN function. In this paper, a CDN manager is proposed to adapt the virtualized CDN function to current and future demand. A Big Data architecture, fulfilling the ETSI NFV guide lines, allows controlling virtualized components while collecting and pre-processing data. Optimization problems minimize CDN costs while ensuring the highest quality. Re-optimization is triggered based on threshold violations; data stream mining sketches transform collected into modeled data and statistical linear regression and machine learning techniques are proposed to produce estimation of future scenarios. Exhaustive simulation over a realistic scenario reveals remarkable costs reduction by dynamically reconfiguring the CDN.
['Marc Ruiz', 'Martin German', 'Luis M. Contreras', 'Luis Velasco']
Big Data-backed video distribution in the telecom cloud