DeePref: Deep Reinforcement Learning For Video Prefetching In Content
  Delivery Networks
Nawras Alkassab Chin-Tser Huang Tania Lorido Botran
Content Delivery Networks carry the majority of Internet traffic, and the
increasing demand for video content as a major IP traffic across the Internet
highlights the importance of caching and prefetching optimization algorithms.
Prefetching aims to make data available in the cache before the requester
places its request to reduce access time and improve the Quality of Experience
on the user side. Prefetching is well investigated in operating systems,
compiler instructions, in-memory cache, local storage systems, high-speed
networks, and cloud systems. Traditional prefetching techniques are well
adapted to a particular access pattern, but fail to adapt to sudden variations
or randomization in workloads. This paper explores the use of reinforcement
learning to tackle the changes in user access patterns and automatically adapt
over time. To this end, we propose, DeePref, a Deep Reinforcement Learning
agent for online video content prefetching in Content Delivery Networks.
DeePref is a prefetcher implemented on edge networks and is agnostic to
hardware design, operating systems, and applications. Our results show that
DeePref DRQN, using a real-world dataset, achieves a 17% increase in
prefetching accuracy and a 28% increase in prefetching coverage on average
compared to baseline approaches that use video content popularity as a building
block to statically or dynamically make prefetching decisions. We also study
the possibility of transfer learning of statistical models from one edge
network into another, where unseen user requests from unknown distribution are
observed. In terms of transfer learning, the increase in prefetching accuracy
and prefetching coverage are [$30%$, $10%$], respectively. Our source code will
be available on Github.