Fine-grained HTTP/3 prioritization via reinforcement learning

Abstract

As the latest version of HTTP, HTTP/3 reduces the loading time of web pages and improves the user experience by replacing TCP and TLS with QUIC. Previous studies have already demonstrated the importance of prioritization for optimizing the performance of HTTP. By default, HTTP/3 schedules all the requests in a round-robin (RR) way on the server. However, in practice, in addition to the different characteristics of web resources, the differences and dynamic changes of network conditions and user equipment (e.g., mobile and PC) will also have a great impact on prioritization. Without considering these factors, existing prioritization schemes deployed on both clients and servers cannot always ensure optimal performance for HTTP/3. In light of the above issues, in this paper, we proposed Dynamic Resources Prioritization via Reinforcement Learning (DRP-RL) to provide fine-grained resource prioritization for HTTP/3 by considering the effects of both network and user equipment. Reinforcement learning (RL) is adopted, in which the RL agent can leverage network, user and web page information to learn the best prioritization of resources across different user groups. The HTTP/3 server is instructed to send the resources in a particular order for different clients dynamically to improve performance. DRP-RL has been implemented based on quic-go, and extensive evaluations indicate that DRP-RL minimizes 3.1%∼21.0% of First Content Paint (FCP) and saves 5.3%∼23.7% of Page Load Time (PLT) across various web pages when compared with RR.