Scalable Distributed Video-on-Demand: Theoretical Bounds and Practical Algorithms

TL;DR

This paper investigates the theoretical limits and practical algorithms for scalable distributed Video-on-Demand systems, focusing on video storage, request handling, and adversarial request scenarios.

Contribution

It introduces a combined model of video allocation and connection scheduling algorithms to analyze scalability under adversarial demand sequences.

Findings

Derived bounds for system scalability

Proposed algorithms for video allocation and scheduling

Validated scalability conditions under adversarial requests

Abstract

We analyze a distributed system where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less. We explore under which conditions such a system can be scalable while serving any sequence of demands. We model this problem through a combination of two algorithms: a video allocation algorithm and a connection scheduling algorithm. The latter plays against an adversary that incrementally proposes video requests.