Configanator: A Data-driven Approach to Improving CDN Performance

The web serving protocol stack is constantly evolving to tackle the technological shifts in networking infrastructure and website complexity. As a result of this evolution, web servers can use a plethora of protocols and configuration parameters to address a variety of realistic network conditions. Yet, today, despite the significant diversity in end-user networks and devices, most content providers have adopted a "one-sizefits-all" approach to configuring the networking stack of their user-facing web servers (or at best employ moderate tuning). In this paper, we demonstrate that the status quo results in sub-optimal performance and argue for a novel framework that extends existing CDN architectures to provide programmatic control over a web server's configuration parameters. We designed a data-driven framework, Configanator, that leverages data across connections to identify their network and device characteristics, and learn the optimal configuration parameters to improve end-user performance. We evaluate Configanator on five traces, including one from a global content provider, and evaluate the performance improvements for real users through two live deployments. Our results show that Configanator improves tail (p95) web performance by 32-67% across diverse websites and networks.