  Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous
streams of heavy-duty mission-critical processing tasks, such as real-time
obstacle detection and navigation. However, in practical applications, erratic
patterns in channel quality, network load, and edge server load can interrupt
the task flow execution, which necessarily leads to severe disruption of the
system's key operations. Existing work has mostly tackled the problem with
reactive approaches, which cannot guarantee task-level reliability. Conversely,
in this paper we focus on learning-based predictive edge computing to achieve
self-resilient task offloading. By conducting a preliminary experimental
evaluation, we show that there is no dominant feature that can predict the
edge-MAS system reliability, which calls for an ensemble and selection of
weaker features. To tackle the complexity of the problem, we propose SeReMAS, a
data-driven optimization framework. We first mathematically formulate a
Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple
edge servers for redundancy, and needs to select which server(s) to transmit
its computing tasks in order to maximize the probability of task execution
while minimizing channel and edge resource utilization. We then create a
predictor based on Deep Reinforcement Learning (DRL), which produces the
optimum task assignment based on application-, network- and telemetry-based
features. We prototype SeReMAS on a testbed composed by a drone, mounting a
PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi
interfaces. We extensively evaluate SeReMAS by considering an application where
one drone offloads high-resolution images for real-time analysis to three edge
servers on the ground. Experimental results show that SeReMAS improves task
execution probability by $17\%$ with respect to existing reactive-based
approaches.
