Performance Modeling, Analysis, and Optimization of Delayed Mobile Data Offloading for Mobile Users

Operators have recently resorted to Wi-Fi offloading to deal with increasing data demand and induced congestion. Researchers have further suggested the use of delayed offloading: if no Wi-Fi connection is available, (some) traffic can be delayed up to a given deadline or until WiFi becomes available. Nevertheless, there is no clear consensus as to the benefits of delayed offloading, with a couple of recent experimental studies largely diverging in their conclusions, nor is it clear how these benefits depend on network characteristics (e.g., Wi-Fi availability), user traffic load, and so on. In this paper, we propose a queueing analytic model for delayed offloading, and derive the mean delay, offloading efficiency, and other metrics of interest, as a function of the user's patience, and key network parameters for two different service disciplines (First Come First Served and Processor Sharing). We validate the accuracy of our results using a range of realistic scenarios and real data traces. Finally, we use these expressions to show how the user could optimally choose deadlines by solving the variations of a constrained optimization problem, in order to maximize her own benefits.