Alohamora: Reviving HTTP/2 Push and Preload by Adapting Policies On the Fly
Alohamora: Reviving HTTP/2 Push and Preload by Adapting Policies On the Fly
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Nikhil Kansal;M. Ramanujam;R. Netravali
中科院分区：
文献类型：
--
作者：
Nikhil Kansal;M. Ramanujam;R. Netravali
尽管他们承诺，HTTP/2的服务器推送和预紧功能的采用最少。在环境中概括我们是难以捉摸的。尽管一个网站提供了大量页面，并且要考虑给定页面的潜在政策空间很大，但Alohamora介绍了几项关键创新：一种页面群集策略，可有利地平衡推送/预加载洞察力的洞察力提取与培训所需的页面数量，以及一个忠实的页面负载模拟器，可以在几毫秒内评估策略（与实际浏览器的10秒相比）。比最近的推送/预加载系统比19-61％的人提供3.6-4倍的好处，并适当适应永不降低的性能。
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 Alo-hamora, 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.