Year: 2024
Type: conference-paper
Abstract: We present the position that the time for general, scalable, privacy-preserving web cache speculation is at hand. At its core, web speculation is about predicting what resources users will want. Machine learning (ML) seems like a natural fit for this predictive task. We show, however, that ML is not fundamentally necessary to achieve a ~10% performance improvement worldwide across a diverse set of sites. We also present a set of constraints that large-scale CDNs are subject to, how these constraints limit the possibilities of using ML, what we did instead to achieve our performance results, and a discussion on the place of ML in large, Internet-scale, systems.
Authors: Isaac Khor, Suleman Ahmad, Avani Wildani
Institution: Northeastern University
Language: English
Topic: Caching and Content Delivery
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4405845684