Figo: Mobility-Aware In-Flight Service Assignment and Reconfiguration with Deep Q-Learning

Today, on-board passengers desire to have in-flight services such as Voice-over-IP (VoIP) and video streaming. These services are usually hosted by geographically distributed Data Centers (DCs) that are built/rented by the airline companies. Flights can be connected to these DCs using two types of Air-to-Ground (A2G) communication alternatives: i) satellite (SC), and ii) Direct-Air-to-Ground connections (DA2G). These two options are different in terms of propagation delay, capacity, and availability. Focusing on reducing the delay of the inflight services, each airplane should be assigned to a nearby DC. However, due to the mobility of flights, a permanent DC assignment might not lead to an acceptable service delay for the flight duration. Therefore, the flight needs to be reassigned to another DC (reconfiguration) along its route, which comes with a cost. The real challenge in this work is to find the best assignments of each airplane to DC(s) and determine the required reconfigurations such that the sum of routing and reconfiguration delay is minimized. We model this problem as a Multi-Period Generalized Assignment Problem (MPGAP) and formulate it as an Integer Linear Programming (ILP) optimization model. To overcome the scalability issues of the ILP, we propose Figo, a flight control framework that solves the MPGAP problem using deep Q-learning. Considering a realistic European-based Space-Air-Ground-Integrated Network (SAGIN) and a real set of flights, we compare the performance of Figo against the optimal. The results indicate that Figo can achieve 7% optimality gap in the worst case, while reducing the runtime from hours to seconds.