Video Delivery Performance of a Large-Scale VoD System and the Implications on Content Delivery

International audienceVideo delivery performance is the main factor that affects Internet video quality. Characterizing the video delivery performance, especially the delivery throughput, can help content providers as well as Internet service providers (ISPs) in system optimization and network planning. Based on a unique dataset consisting of 20 million video download speed measurements , this paper comprehensively studies the video delivery throughput of a large-scale commercial video-on- demand (VoD) system. We observe that user speed exhibits a large variation over time of day as well as across provincial locations. In particular, the worst performance of day is 30% lower than the peak performance . The analysis also reveals that video download speed has a notable impact on Internet video quality, which in turn influences user engagement . The impact, however, becomes limited when the speed increases beyond a certain threshold, which is mostly dependent on the video encoded bitrates. We further examine the interaction between Internet infrastructure and video delivery throughput using the linear regression model and find that crossing the ISP or regional network border yields 15-20% speed loss. Based on these observations , we finally evaluate the potential of edge caching and hybrid CDN-P2P in the improvement of video download performance and video quality

Machine Learning for Next-generation Content Delivery Networks: Deployment, Content Placement, and Performance Management

With the explosive demands for data and the growth in mobile users, content delivery networks (CDNs) are facing ever-increasing challenges to meet end-users quality-of-experience requirements, ensure scalability and remain cost-effective. These challenges encourage CDN providers to seek a solution by considering the new technologies available in today’s computer network domain. Network Function Virtualization (NFV) is a relatively new network service deployment technology used in computer networks. It can reduce capital and operational costs while yielding flexibility and scalability for network operators. Thanks to the NFV, the network functions that previously could be offered only by specific hardware appliances can now run as Virtualized Network Functions (VNF) on commodity servers or switches. Moreover, a network service can be flexibly deployed by a chain of VNFs, a structure known as the VNF Forwarding Graph or VNF-FG. Considering these advantages, the next-generation CDN will be deployed using NFV infrastructure. However, using NFV for service deployment is challenging as resource allocation in a shared infrastructure is not easy. Moreover, the integration of other paradigms (e.g., edge computing and vehicular network) into CDN will compound the complexity of content placement and performance management for the next-generation CDNs. In this regard, due to their impacts on final service and end-user perceived quality, the challenges in service deployment, content placement, and performance management should be addressed carefully. In this thesis, advanced machine learning methods are utilized to provide algorithmic solutions for the abovementioned challenges of the next generation CDNs. Regarding the challenges in the deployment of the next-generation CDNs, we propose two deep reinforcement learning-based methods addressing the joint problems of VNF-FG’s composition and embedding, as well as function scaling and topology adaptation. As for content placement challenges, a deep reinforcement learning-based approach for content migration in an edge-based CDN with vehicular nodes is proposed. The proposed approach takes advantage of the available caching resources in the proximity of the full local caches and efficiently migrates contents at the edge of the network. Moreover, for managing the performance quality of an operating CDN, an unsupervised machine learning anomaly detection method is provided. The proposed method uses clustering to enable easier performance analysis for next-generation CDNs. Each proposed method in this thesis is evaluated by comparison to the state-of-the-art approaches. Moreover, when applicable, the optimality gaps of the proposed methods are investigated as well