PROJECT TITLE :

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

ABSTRACT:

To deal with recent exponential increases in demand for mobile information, wireless Internet service suppliers (ISPs) are increasingly changing their pricing plans and deploying Wi-Fi hotspots to dump their mobile traffic. However, these ISP-centric approaches for traffic management don't continually match the interests of mobile users. Users face a complicated, multi-dimensional tradeoff between cost, throughput, and delay in creating their offloading selections: whereas they may save money and receive a better throughput by awaiting Wi-Fi access, they'll not wait for Wi-Fi if they are sensitive to delay. To navigate this tradeoff, we have a tendency to develop Adaptive bandwidth Management through USer-Empowerment (AMUSE), a functional prototype of a sensible, value-aware Wi-Fi offloading system that takes under consideration a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and Wi-Fi 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 have a tendency to introduce a replacement receiver-aspect bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates in keeping with their own price-throughput-delay tradeoff while not relying on support from totally different apps' content servers. Through a measurement study of 20 smartphone users' traffic usage traces, we tend to observe that though users already offload a large amount of some application types, our framework can offload a important further portion of users' cellular traffic. We have a tendency to implement AMUSE on Windows seven tablets and evaluate its effectiveness with 3G and Wi-Fi usage data obtained from an attempt with 37 mobile users. Our results show that AMUSE improves user utility; compared with AMUSE, different offloading algorithms yield 14 and 27 % lower user utilities for lightweight and heavy users, respectively. Intelligently managing users' competing interes- s for cost, throughput, and delay will thus improve their offloading decisions.