# AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs

## TL;DR

AMUSE is a practical system that empowers mobile users to make cost-aware offloading decisions by balancing throughput and delay, improving user utility over existing methods.

## Contribution

It introduces a novel user-centric offloading framework that considers individual throughput-delay tradeoffs and enforces bandwidth allocation for TCP flows.

## Key findings

- AMUSE significantly increases cellular offloading without user intervention.
- The system improves user utility by 14-27% compared to other algorithms.
- A measurement study with 37 users validates the effectiveness of AMUSE.

## Abstract

To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying WiFi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for WiFi access, they may not wait for WiFi if they are sensitive to delay. To navigate this tradeoff, we develop AMUSE (Adaptive bandwidth Management through USer-Empowerment), a functional prototype of a practical, cost-aware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps' content servers. Through a measurement study of 20 smartphone users' traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users' cellular traffic. We implement AMUSE and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility, when compared with AMUSE, other offloading algorithms yield 14\% and 27\% lower user utilities for light and heavy users, respectively.