Request Patterns and Caching for VoD Services with Recommendation Systems

TL;DR

This paper models user request patterns in VoD services influenced by recommendation engines using a Markovian approach, and explores caching strategies to optimize bandwidth and QoS.

Contribution

It introduces a Markovian request model that captures time-correlation due to recommendations and analyzes pre-fetching trade-offs for content caching.

Findings

Markovian model aligns with empirical request data.

Pre-fetching based on recommendations can improve QoS.

Trade-offs between bandwidth use and latency are quantified.

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 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.