ConfigTron — Tackling network diversity with heterogenous configuration.

The web serving protocol stack is constantly changing and evolving to tackle technological shifts in networking infrastructure and website complexity. For example, development of QuiC to tackle security issues, HTTP/2 to tackle loss, and BBR to tackle high throughput. As a result of this evolution, the web serving stack includes a plethora of protocols and configuration parameters that enable the web serving stack to address a variety of realistic network conditions. Yet, today, most content providers have adopted a “one-size-fits-all” approach to configuring the networking stack of their user facing web servers, despite the diversity in end-user networks and devices.

In this paper, we demonstrate through empirical evidence that this “one-size-fits-all” approach results in sub-optimal performance and argue for a novel framework that extends existing CDN architectures to provide programmatic control over the configuration options of the CDN serving stack. To this end, we designed ConfigTron a data-driven framework that leverages data from all connections to identify their network characteristics and learn the optimal configuration parameters to improve end user performance.
ConfigTron uses multi-armed bandit-based learning algorithm to find optimal configurations in minimal time, enabling a CSP to systematically explore heterogeneous configurations while improving end-user page load time by as much as 16% (750ms) on median.