Parallel Route Optimization and Service Assurance in Energy-Efficient Software-Defined Industrial IoT Networks

In recent years, the Industrial world has been embracing new digital technology, including the internet of things (IoT) paradigm that promises revolutionizing-prospects in numerous industrial applications. However, many deployment challenges related to real-time big data analytics, service assurance, resource optimization, energy consumption, and security awareness are raised. In this work, we focus on service assurance and resource optimization, including energy consumption challenges over Industrial Internet of Things (IIoT)-based environments since the existing network routing algorithms cannot meet the strict heterogeneous quality of service (QoS) requirements of industrial communications while optimizing resources. We take advantage of the flexibility and programmability offered by the promising software-defined networking paradigm, and we propose a centralized route optimization and service assurance scheme, named ROSA, over a multi-layer programmable industrial architecture. The proposed solution supports a wide range of heterogeneous flows, such as ultra-reliable low-latency communications (URLLC) and bandwidth-sensitive services. The routing optimization problems are formulated as multi-constrained shortest path problems. The Lagrangian Relaxation approach is used to solve the . Hence, we deploy a pair of parallel routing algorithms run according to the flow type to ensure QoS requirements, efficiently allocate constrained resources, and enhance the overall network energy consumption. We conduct extensive simulations to validate the proposed ROSA scheme. The experimental results show promising performance in terms of reducing bandwidth utilization by up to 22%, end-to-end delay at least by 21%, packet loss by more than 19%, flow violation by about 16%, and energy consumption up to 14% as compared to well-known benchmarks in QoS provisioning and energy-aware routing problem.