To Block or Not to Block: Accelerating Mobile Web Pages On-The-Fly Through JavaScript Classification
Moumena Chaqfeh, Muhammad Haseeb, Waleed Hashmi, Patrick Inshuti,, Manesha Ramesh, Matteo Varvello, Fareed Zaffar, Lakshmi Subramanian, Yasir, Zaki

TL;DR
SlimWeb is a machine learning-based system that dynamically classifies and blocks unnecessary JavaScript on mobile web pages, significantly reducing load times and improving user experience in developing regions.
Contribution
It introduces a novel on-the-fly JavaScript classification method using supervised ML to optimize mobile web page performance.
Findings
50% reduction in page load time
Over 30% improvement over competing solutions
90% median accuracy in JavaScript classification
Abstract
The increasing complexity of JavaScript in modern mobile web pages has become a critical performance bottleneck for low-end mobile phone users, especially in developing regions. In this paper, we propose SlimWeb, a novel approach that automatically derives lightweight versions of mobile web pages on-the-fly by eliminating the use of unnecessary JavaScript. SlimWeb consists of a JavaScript classification service powered by a supervised Machine Learning (ML) model that provides insights into each JavaScript element embedded in a web page. SlimWeb aims to improve the web browsing experience by predicting the class of each element, such that essential elements are preserved and non-essential elements are blocked by the browsers using the service. We motivate the core design of SlimWeb using a user preference survey of 306 users and perform a detailed evaluation of SlimWeb across 500 popular…
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Taxonomy
TopicsICT in Developing Communities · Mobile Health and mHealth Applications · Green IT and Sustainability
