AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs
Youngbin Im, Carlee Joe-Wong, Sangtae Ha, Soumya Sen, Ted Taekyoung, Kwon, Mung Chiang
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.
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…
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.