Alohamora: Reviving HTTP/2 Push and Preload by Adapting Policies On the Fly


Nikhil Kansal, Murali Ramanujam, and Ravi Netravali, UCLA


Despite their promise, HTTP/2's server push and preload features have seen minimal adoption. The reason is that the efficacy of a push/preload policy depends on subtle relationships between page content, browser state, device resources, and network conditions—static policies that generalize across environments remain elusive. We present Alohamora, a system that uses Reinforcement Learning to learn (and apply) the appropriate push/preload policy for a given page load based on inputs characterizing the page structure and execution environment. To ensure practical training despite the large number of pages served by a site and the massive space of potential policies to consider for a given page, Alohamora introduces several key innovations: a page clustering strategy that favorably balances push/preload insight extraction with the number of pages required for training, and a faithful page load simulator that can evaluate a policy in several milliseconds (compared to 10s of seconds with a real browser). Experiments across a wide range of pages and mobile environments (emulation and real-world) reveal that Alohamora accelerates page loads by 19-61%, provides 3.6-4× more benefits than recent push/preload systems, and properly adapts to never degrade performance.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

This content is available to:

@inproceedings {262005,
title = {Alohamora: Reviving HTTP/2 Push and Preload by Adapting Policies On the Fly},
booktitle = {18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21)},
year = {2021},
url = {},
publisher = {{USENIX} Association},
month = apr,
Kansal Paper (Prepublication) PDF