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Content Delivery Networks carry the majority of Internet traffic, and theincreasing demand for video content as a major IP traffic across the Internethighlights the importance of caching and prefetching optimization algorithms.Prefetching aims to make data available in the cache before the requesterplaces its request to reduce access time and improve the Quality of Experienceon the user side. Prefetching is well investigated in operating systems,compiler instructions, in-memory cache, local storage systems, high-speednetworks, and cloud systems. Traditional prefetching techniques are welladapted to a particular access pattern, but fail to adapt to sudden variationsor randomization in workloads. This paper explores the use of reinforcementlearning to tackle the changes in user access patterns and automatically adaptover time. To this end, we propose, DeePref, a Deep Reinforcement Learningagent for online video content prefetching in Content Delivery Networks.DeePref is a prefetcher implemented on edge networks and is agnostic tohardware design, operating systems, and applications. Our results show thatDeePref DRQN, using a real-world dataset, achieves a 17% increase inprefetching accuracy and a 28% increase in prefetching coverage on averagecompared to baseline approaches that use video content popularity as a buildingblock to statically or dynamically make prefetching decisions. We also studythe possibility of transfer learning of statistical models from one edgenetwork into another, where unseen user requests from unknown distribution areobserved. In terms of transfer learning, the increase in prefetching accuracyand prefetching coverage are [$30%$, $10%$], respectively. Our source code willbe available on Github.

DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks