Modeling Request Patterns in VoD Services with Recommendation Systems

Abstract

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 request patterns in VoD services. We propose a Markovian request model to capture the timecorrelation 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 prefetch (fetch before contents are requested) and cache content likely to be requested 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 prefetching.

Show me the Cache: Optimizing Cache-Friendly Recommendations for Sequential Content Access

“…Our answer to this question follows from two key observations: (i) the performance of a caching algorithm is dependent on user request patterns; (ii) user requests are increasingly driven by recommendation algorithms [20], [21], [22]. For example, Netflix reports that around 80% of its video views are through recommendations [23], while the corresponding percentage for YouTube's related video is 50% [22].…”