Topology-Aware Cluster Configuration for Real-time Multi-access Edge Computing

We consider data-intensive real-time systems, such as mission-critical data-intensive applications such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., which demand relatively low response time in processing data from IoT (Internet of Things) devices. Usually, in such cases, the edge computing paradigm is leveraged to drastically reduce the processing delay of such applications by performing the computations on edge devices placed closer to the data sources, i.e., the IoT devices. However, most edge devices, such as cellular phones, tablets, and UAVs (Unmanned Aerial Vehicles), are mobile in nature. Hence, the cluster configuration must be dynamically adapted with respect to the changing network topology of the edge cluster such that the observed overall communication delay incurred by the edge devices in processing the data from the IoT devices is minimized. To that end, we propose Deep Reinforcement Learning-based intelligent assignment of IoT devices to non-stationary edge devices such that the communication delay is minimized and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.