Reinforcement Learning for Real-Time Multi-Access Edge Computing

We examine data-intensive real-time applications, such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., that require relatively low response time in processing data from the Internet of Things (IoT) devices. Typically, in such circumstances, the edge computing paradigm is utilised to drastically reduce the processing delay of such applications. However, with the growing IoT devices, the edge device cluster needs to be configured properly such that the real-time requirements are met. Therefore, the cluster configuration must be dynamically adapted to the changing network topology of the edge cluster in order to minimise the observed overall communication delay incurred by edge devices when processing data from IoT devices. To this end, we propose an intelligent assignment of IoT devices to edge devices based on Reinforcement Learning such that communication delay is minimised and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.