User Mapping Strategy in Multi-CDN Streaming: A Data-Driven Approach

Abstract

Using Content Delivery Networks (CDNs) for video distribution has become the de facto approach for video streaming today, because they are easy to use (e.g., video chunks can be delivered as files via HTTP) and have good scalability. Today, it has become the norm rather than the exception for video providers to hire multiple CDNs for their video services in a pay-per-use manner—not only to serve users at different locations, but also to reduce operational costs. Given that multiple CDNs and their peering servers exist at many different locations, selecting different CDNs for different users in the same online video system has become a critical decision that can significantly affect the users’ quality of experience (QoE). Conventional strategies are generally rule-based, e.g., assigning users to CDNs according to their locations or ISPs, but cannot guarantee any particular QoE level because QoE is affected by a combination of complicated factors. In this paper, we propose using a data-driven approach to study the factors determining users’ QoE, including both quality of service (QoS) and user factors. Our findings indicate that QoE is affected by both the QoS provided by the CDNs and user preferences for the video content. We design a machine learning-based predictive model to capture the “utility” of a video for a user given a particular QoS guarantee. Based on that, we strategically assign CDNs to users to maximize the overall QoE. One month trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.