Deep reinforcement learning for adaptive network slicing in 5g for intelligent vehicular systems and smart cities

Abstract

Systems and methods for processing a service request within a network environment can include a first cluster of fog nodes that execute service tasks. The cluster can include a primary fog node and nearest neighbor fog nodes. The primary fog node can receive, from the network, a service request, determine service request resource data that includes a first time, quantity of resource blocks required to serve the request, and a hold time required to serve the request locally. An edge controller, connected to the network and the first cluster, can receive, from the primary fog node, the service request resource data, identify available resources at the nearest neighbor fog nodes and the primary fog node, and determine whether resource blocks are available to fulfill the service request using deep reinforcement learning algorithms. The edge controller can also refer a rejected service request to a cloud computing system for execution.