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Video on Demand (VoD) services like Netflix and YouTube account for everincreasing fractions of Internet traffic. It is estimated that this fractionwill cross 80% in the next three years. Most popular VoD services haverecommendation engines which recommend videos to users based on their viewinghistory, thus introducing time-correlation in user requests. Understanding andmodeling this time-correlation in user requests is critical for network trafficengineering. The primary goal of this work is to use empirically observedproperties of user requests to model the effect of recommendation engines onthe request patterns in VoD services. We propose a Markovian request model tocapture the time-correlation in user requests and show that our model isconsistent with the observations of existing empirical studies.  Most large-scale VoD services deliver content to users via a distributednetwork of servers as serving users requests via geographically co-locatedservers reduces latency and network bandwidth consumption. The contentreplication policy, i.e., determining which contents to cache on the servers isa key resource allocation problem for VoD services. Recent studies show thatlow start-up delay is a key Quality of Service (QoS) requirement of users ofVoD services. This motivates the need to pre-fetch (fetch before contents arerequested) and cache content likely to be request in the near future. Sincepre-fetching leads to an increase in the network bandwidth usage, we use ourMarkovian model to explore the trade-offs and feasibility of implementingrecommendation based pre-fetching.

Request Patterns and Caching for VoD Services with Recommendation Systems