Steering Traffic via Recurrent Neural Networks in Challenged Edge Scenarios

Abstract

With edge computing, it is possible to offload computationally intensive tasks to closer and more powerful servers, passing through an edge network. This practice aims to reduce both response time and energy consumption of data-intensive applications, crucial constraints in mobile and IoT devices. In challenged networked scenarios, such as those deployed by first responders after a natural or human-made disaster, it is particularly challenging to achieve high levels of throughput due to scarce network conditions.In this paper, we present an algorithm for traffic management that takes advantage of a deep learning model to implement the forwarding mechanism during task offloading in these challenging scenarios. In particular, our work explores if and when it is worth using deep learning on a switch to route traffic generated by microservices and offloading requests. Our approach differs from classical ones in the design: we do not train centralized routing decisions. Instead, 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.