Software-defined Dynamic 5G Network Slice Management for Industrial Internet of Things

Abstract

This paper addresses the challenges of delivering fine-grained Quality of Service (QoS) and communication determinism over 5G wireless networks for real-time and autonomous needs of Industrial Internet of Things (IIoT) applications while effectively sharing network resources. Specifically, this work presents DANSM, a dynamic and autonomous network slice management middleware for 5G-based IIoT use cases, such as adaptive robotic repair. The novelty of our approach lies in (1) its use of multiple M/M/1 queues to formulate a 5G network resource scheduling optimization problem comprising service-level and system-level objectives; (2) the design of a heuristics-based solution to overcome the NP-hard properties of this optimization problem, and (3) how it dynamically and autonomously provisions and manages 5G network slices used to deliver predictable communications to IIoT use cases. The results of experiments evaluating DANSM on our testbed comprising a Free5GC-based core and UERANSIMbased simulations reveal that it can efficiently balance the traffic load in the data plane thereby reducing the end-to-end response time and improve the service performance by finishing 34% more subtasks than Modified Greedy Algorithm (MGA), 64% more subtasks than First Fit Descending (FFD) approach and 22% more subtasks than Best Fit Descending (BFD) approach while minimizing the operational costs.