Semantic Caching for Improving Web Affordability
Hafsa Akbar, Danish Athar, Muhammad Ayain Fida Rana, Chaudhary Hammad Javed, Zartash Afzal Uzmi, Ihsan Ayyub Qazi, Zafar Ayyub Qazi

TL;DR
This paper introduces a semantic caching approach using Large Language Models to reduce data transfer for web images, significantly improving web affordability especially in high-cost data regions.
Contribution
It presents a novel semantic caching method leveraging LLMs to identify replaceable images, demonstrating substantial data savings and evaluating multiple models for practical deployment.
Findings
Up to 37% of images are replaceable in some websites
Approximately 10% additional byte savings over exact caching
LLaMA 3.1 performs comparably to GPT-4o in semantic replaceability assessment
Abstract
The rapid growth of web content has led to increasingly large webpages, posing significant challenges for Internet affordability, especially in developing countries where data costs remain prohibitively high. We propose semantic caching using Large Language Models (LLMs) to improve web affordability by enabling reuse of semantically similar images within webpages. Analyzing 50 leading news and media websites, encompassing 4,264 images and over 40,000 image pairs, we demonstrate potential for significant data transfer reduction, with some website categories showing up to 37% of images as replaceable. Our proof-of-concept architecture shows users can achieve approximately 10% greater byte savings compared to exact caching. We evaluate both commercial and open-source multi-modal LLMs for assessing semantic replaceability. GPT-4o performs best with a low Normalized Root Mean Square Error of…
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Taxonomy
TopicsCaching and Content Delivery · Peer-to-Peer Network Technologies · Web Data Mining and Analysis
