Semantic Caching for Improving Web Affordability

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 0.1735 and a weighted F1 score of 0.8374, while the open-source LLaMA 3.1 model shows comparable performance, highlighting its viability for large-scale applications. This approach offers benefits for both users and website operators, substantially reducing data transmission. We discuss ethical concerns and practical challenges, including semantic preservation, user-driven cache configuration, privacy concerns, and potential resistance from website operators