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User Mapping Strategies in Multi-Cloud Streaming: A Data-Driven Approach

Using content delivery networks (CDNs) for video distribution has become a de facto approach for today's video streaming, due to the easy usage and good scalability. Today, it has become a norm rather than an exception for video providers to hire multiple cloud CDNs for their video services in a pay-per-use manner, to not only serve users at different locations, but also reduce the operation costs. Given the multiple CDNs and their peering servers at many different locations, mapping a user to an edge CDN server has become a critical decision that can affect the quality of experience (QoE) of users. Conventional user mapping strategies are generally rule-based, e.g., assigning users to CDN servers according to only their locations or ISPs, which cannot guarantee any QoE. In this paper, we first propose to use a data-driven approach to study factors determining the streaming QoE in the multi-cloud CDN paradigm. Our findings suggest that the streaming QoE is affected by a combination of not only network factors but also user factors including their preference of video content. Then, we design a machine learning based predictive model to capture the QoE given the network conditions and user preference. Finally, we formulate the user mapping problem as an optimization problem and design algorithms to solve it: our algorithms identify users whose QoE are mostly affected by QoS and assign users to CDN servers so that the overall QoE can be maximized. Trace-driven experiments further verify the effectiveness of our design.