Pros & Cons of Model-based Bandwidth Control for Client-assisted Content Delivery

TL;DR

This paper evaluates server bandwidth control strategies in client-assisted content delivery, introducing a model-based approach that uses offline measurements to optimize performance, outperforming static and dynamic methods under certain conditions.

Contribution

It presents a novel model-based bandwidth control method relying on offline measurements, demonstrating its effectiveness over traditional static and dynamic strategies.

Findings

Model-based control outperforms static and dynamic approaches.

Static and dynamic controllers can be suboptimal.

Model-based approach's broad applicability may be limited by measurement overhead.

Abstract

A key challenge in \cacd\ is determining how to allocate limited server bandwidth across a large number of files being concurrently served so as to optimize global performance and cost objectives. In this paper, we present a comprehensive experimental evaluation of strategies to control server bandwidth allocation. As part of this effort, we introduce a new {\em model-based} control approach that relies on an accurate yet concise "cheat sheet" based on a priori offline measurement to predict swarm performance as a function of the server bandwidth and other swarm parameters. Our evaluation using a prototype system, \cs, instantiating static, dynamic, and model-based controllers shows that static and dynamic controllers can both be suboptimal due to different reasons. In comparison, a model-based approach consistently outperforms both static and dynamic approaches provided it has access to detailed measurements in the regime of interest. Nevertheless, the broad applicability of a model-based approach may be limited in practice because of the overhead of developing and maintaining a comprehensive measurement-based model of swarm performance in each regime of interest.