Bandwidth Allocation Games

TL;DR

This paper models and analyzes bandwidth throttling plans as an optimization and game-theoretic problem, proposing fair and tractable solutions for resource allocation among users with different data needs.

Contribution

It introduces a novel optimization framework for threshold-based bandwidth plans and extends it to a game-theoretic setting demonstrating convergence and fairness.

Findings

Thresholding mechanisms effectively modulate bandwidth consumption.

The proposed models yield fair and computationally tractable solutions.

Game dynamics converge to stable, fair allocations.

Abstract

Internet providers often offer data plans that, for each user's monthly billing cycle, guarantee a fixed amount of data at high rates until a byte threshold is reached, at which point the user's data rate is throttled to a lower rate for the remainder of the cycle. In practice, the thresholds and rates of throttling can appear and may be somewhat arbitrary. In this paper, we evaluate the choice of threshold and rate as an optimization problem (regret minimization) and demonstrate that intuitive formulations of client regret, which preserve desirable fairness properties, lead to optimization problems that have tractably computable solutions. We begin by exploring the effectiveness of using thresholding mechanisms to modulate overall bandwidth consumption. Next, we separately consider the regret of heterogeneous users who are {\em streamers}, wishing to view content over a finite period of fixed rates, and users who are {\em file downloaders}, desiring a fixed amount of bandwidth per month at their highest obtainable rate. We extend our analysis to a game-theoretic setting where users can choose from a variety of plans that vary the cap on the unbounded-rate data, and demonstrate the convergence of the game. Our model provides a fresh perspective on a fair allocation of resources where the demand is higher than capacity, while focusing on the real-world phenomena of bandwidth throttling practiced by ISPs. We show how the solution to the optimization problem results in allocations that exhibit several desirable fairness properties among the users between whom the capacity must be partitioned.