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

arXiv · 2106.13764

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

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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 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.

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BibTeX RIS

Moumena Chaqfeh, Muhammad Haseeb, Waleed Hashmi, Patrick Inshuti, Manesha Ramesh, Matteo Varvello, Fareed Zaffar, Lakshmi Subramanian, Yasir Zaki. 2021-06-20. To Block or Not to Block: Accelerating Mobile Web Pages On-The-Fly Through JavaScript Classification. https://arxiv.org/abs/2106.13764

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