ADELE: An Architecture for Steering Traffic and Computations via Deep Learning in Challenged Edge Networks

Edge computing allows computationally intensive tasks to be offloaded to nearby (more) powerful servers, passing through an edge network. The goal of such offloading is to reduce data-intensive application response time or energy consumption, crucial constraints in mobile and IoT devices. In challenged networked scenarios, such as those deployed by first responders after a natural or man-made disaster, it is particularly difficult to achieve high levels of throughput due to scarce network conditions. In this paper, we present an architecture for traffic management that may use deep learning to support forwarding during task offloading in these challenging scenarios. In particular, our goal is to study if and when it is worth using deep learning to route traffic generated by microservices and offloading requests in these situations. Our design is different than classical approaches that use learning since we do not train for centralized routing decisions, but we let each router learn how to adapt to a lossy path without coordination, by merely using signals from standard performance-unaware protocols such as OSPF. Our results, obtained with a prototype and with simulations are encouraging, and uncover a few surprising results.