  Video on Demand (VoD) services like Netflix and YouTube account for ever
increasing fractions of Internet traffic. It is estimated that this fraction
will cross 80% in the next three years. Most popular VoD services have
recommendation engines which recommend videos to users based on their viewing
history, thus introducing time-correlation in user requests. Understanding and
modeling this time-correlation in user requests is critical for network traffic
engineering. The primary goal of this work is to use empirically observed
properties of user requests to model the effect of recommendation engines on
the request patterns in VoD services. We propose a Markovian request model to
capture the time-correlation in user requests and show that our model is
consistent with the observations of existing empirical studies.
  Most large-scale VoD services deliver content to users via a distributed
network of servers as serving users requests via geographically co-located
servers reduces latency and network bandwidth consumption. The content
replication policy, i.e., determining which contents to cache on the servers is
a key resource allocation problem for VoD services. Recent studies show that
low start-up delay is a key Quality of Service (QoS) requirement of users of
VoD services. This motivates the need to pre-fetch (fetch before contents are
requested) and cache content likely to be request in the near future. Since
pre-fetching leads to an increase in the network bandwidth usage, we use our
Markovian model to explore the trade-offs and feasibility of implementing
recommendation based pre-fetching.
