Multi-Task Learning for Identifying Multi-Activity Situations and Application Type From Network Traffic

Optimizing networks to meet user needs has been a longstanding goal for the various key players in the network sector. To this end, a large number of studies have addressed the case of classifying network traffic into a set of activities (e.g., streaming) and applications (e.g., Spotify). Nonetheless, the fast-paced growth of the digital market has favored the advent of new consuming habits such as the simultaneous performance of multiple activities. This concept is referred to as multi-activity situations or media multitasking. Conceiving solutions that can cope with these emerging consuming patterns may enable network operators and service providers to better adapt their network management solutions and commercial plans. In this paper, we propose a novel approach that can deal with a challenging scenario comprising both single-activity and multi-activity situations. The proposed approach pre-processes a network trace over a time-window and then determines to which situation type it belongs. Furthermore, it identifies the type of the activities being performed and the applications being used (e.g., chat on Facebook & streaming on Spotify). Our experiments highlighted that our solution is able to achieve a satisfactory level of performance despite the complexity of the scenario that we target. Indeed, our obtained results are comparable to state-of-the-art techniques addressing less challenging scenarios that involve only single-activity situations.