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 re- sources, 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 pol- icy 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.

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@inproceedings {262005,
author = {Nikhil Kansal and Murali Ramanujam and Ravi Netravali},
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},
isbn = {978-1-939133-21-2},
pages = {269--287},
url = {},
publisher = {USENIX Association},
month = apr

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