Matrix Factorization for Cache Optimization in Content Delivery Networks (CDN)

TL;DR

This paper explores the application of matrix factorization techniques to predict content popularity in CDNs, aiming to improve cache management and reduce latency.

Contribution

It introduces a novel approach using matrix factorization for CDN cache optimization, demonstrating promising results on real-world data.

Findings

Achieved low root mean square error in popularity prediction

Demonstrated potential for improved cache eviction strategies

Validated approach on real CDN log data

Abstract

Content delivery networks (CDNs) are key components of high throughput, low latency services on the internet. CDN cache servers have limited storage and bandwidth and implement state-of-the-art cache admission and eviction algorithms to select the most popular and relevant content for the customers served. The aim of this study was to utilize state-of-the-art recommender system techniques for predicting ratings for cache content in CDN. Matrix factorization was used in predicting content popularity which is valuable information in content eviction and content admission algorithms run on CDN edge servers. A custom implemented matrix factorization class and MyMediaLite were utilized. The input CDN logs were received from a European telecommunication service provider. We built a matrix factorization model with that data and utilized grid search to tune its hyper-parameters. Experimental results indicate that there is promise about the proposed approaches and we showed that a low root mean square error value can be achieved on the real-life CDN log data.