The web serving protocol stack is constantly changing and evolving to tackle technological shifts in networking infrastructure and website complexity. As a result of this evolution, the 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-size-fits-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, ConfigTron, that leverages data across connections to identify their network and device characteristics, and learn the optimal configuration parameters to improve end-user performance. To demonstrate the scalability and efficiency, we evaluate ConfigTron on five traces, including one from a global content provider. Further, we analyzed ConfigTron using two live-deployments including one on the global content provider. Our results show that ConfigTron improves tail (p95) web performance by 32-67% across diverse websites and networks.

Acknowledgements

We are indebted to the Janusz Jezowicz from SpeedChecker for granting us access to their globally distibuted Internet and web measurements platform.