Selective Traffic Offloading on the Fly: A Machine Learning Approach

It has been well recognized that network transmission constitutes a large portion of smartphone energy consumption, mainly because of the tail energy caused by cellular network interface. Traffic offloading has been proposed to reduce energy by letting a smartphone offload network traffic to its neighbors in vicinity via low-power direct connections (e.g., WiFi Direct or Bluetooth). Our experiments conducted in a realistic environment reveal that energy efficiency cannot be improved or even deteriorates without a carefully designed offloading strategy. In this paper, we propose a selective traffic offloading scheme implemented as a smartphone middleware in a software-defined fashion, which consists of a packet classifier and a traffic scheduler. Using a light-weight machine learning approach exploiting unique smartphone context information, the packet classifier identifies packets generated on the fly as offloadable or not with substantially improved efficiency and feasibility on resource limited smartphones compared to traditional approaches. Both testbed and simulation based experiments are conducted and the results show that our proposal always attains the superior performance on a number of comparison metrics.