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

Abstract

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 on Windows 7 tablets 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. Intelligently managing users' competing interests for cost, throughput, and delay can therefore improve their offloading decisions.

Adaptive Wi-Fi offloading schemes in heterogeneous networks, the survey

“…Another research proposes an offloading system that is cost aware. You do this by using adaptive bandwidth management through user-empowerment (AMUSE), as shown in Figure 4 [46]. From the user perspective, expect the costs incurred by the quality of throughput, and delay the tradeoff of different applications.…”

SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

“…In [16], authors proved that offloading 65% on the spot could potentially save 55% of the battery power of UE. To the betterment of WiFi offloading, a prediction based method is introduced in [17] where in absence of WiFi coverage, the transmission control protocol congestion window is modified in a way to accommodate the traffic.…”

Gaussian Belief Propagation for Solving Network Utility Maximization with Delivery Contracts

“…Due to the characteristic of separability of utility functions, a basic distributed NUM algorithm is derived to maximize aggregate user utility by the dual decomposition theory [12]. Along this way, so many extended NUM models and resultant distributed algorithms have been proposed for network architectural design, cross-layer optimization and resource allocation in wireless as well as wireline networks [14][15][16][17][18][19]. There are some works which studied the extended NUM models with the non-strictly concave or non-concave utility functions such as in [20][21][22][23].…”