The Cask Effect of Multi-source Content Delivery: Measurement and Mitigation

With the explosive growth of Internet traffic, multi-source content delivery has been introduced for improving the performance and quality-of-experience (QoE) of Internet services. Upgrading from single-source content delivery to multi-source content delivery, however, may not always lead to a better performance. Instead, a decline in terms of delivery speed often occurs. By conducting a comprehensive study, we show that the underlying reason of this counter-intuitive phenomenon is actually due to the cask effect of data sources at both macro and micro level. Specifically, at the macro level, data sources with different types are highly heterogeneous in terms of delivery performance, which means data sources with certain types are particularly easy to become the "short boards". At the micro level, for the data sources chosen by a client, the high diversity of participation time (DPT) of the sources could impair the acceleration effect. Motivated by the above findings, we design MDR (Multi-source Delivery Redirector), a middleware that contains two optimizations to improve the acceleration effect. One is the feature-greedy selection algorithm which can avoid selecting data sources with inferior types, and the other is the DPT-driven shuffle strategy which can avoid using unstable data sources. Simulation-based experiments show that the MDR outperforms existing approaches in terms of overall downloading performance.