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 software-defined, 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) the 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) the implementation of a software-defined solution that incorporates the heuristics to dynamically and autonomously provision and manage 5G network slices that deliver predictable communications to IIoT use cases. Empirical studies evaluating DANSM on our testbed comprising a Free5GC-based core and UERANSIM-based simulations reveal that the software-defined DANSM solution can efficiently balance the traffic load in the data plane thereby reducing the end-to-end response time and improve the service performance by completing 34% more subtasks than a Modified Greedy Algorithm (MGA), 64% more subtasks than First Fit Descending (FFD) and 22% more subtasks than Best Fit Descending (BFD) approaches all while minimizing operational costs.