  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 web pages in a developing region on
real 3G and 4G cellular networks, along with a user experience study with 20
real-world users and a usage willingness survey of 588 users. Evaluation
results show that SlimWeb achieves a 50% reduction in the page load time
compared to the original pages, and more than 30% reduction compared to
competing solutions, while achieving high similarity scores to the original
pages measured via a qualitative evaluation study of 62 users. SlimWeb improves
the overall user experience by more than 60% compared to the original pages,
while maintaining 90%-100% of the visual and functional components of most
pages. Finally, the SlimWeb classifier achieves a median accuracy of 90% in
predicting the JavaScript category.
