GAI-Based Resource and QoE Aware Service Placement in Next-Generation Multi-Domain IoT Networks

Network Function Virtualization (NFV) has recently emerged as a highly cost-effective paradigm for flexibly provisioning services in next-generation Internet of Things (IoT) networks, by introducing Service Function Chain (SFC) technology. However, the rapid expansion of network scales and increasing diversification of service requirements in recent years pose significant challenges to ensuring the Quality of Experience (QoE) of users in Next-generation Multi-domain IoT (NMIoT) networks. The effective deployment of SFCs in NMIoT networks to satisfy diversified resource demands while enhancing QoE of users is crucial. The recent breakthroughs in Generative Artificial Intelligence (GAI) technologies bring a new opportunity to deliver customized services and guarantee enhanced service quality in NMIoT networks. To tackle the challenges, in this paper, we investigate the problem of Resource and QoE aware SFC Placement (RQSP) in NMIoT networks. Firstly, we formulate the RQSP problem as a mixed integer linear programming model, taking into account resource demands and Quality of Service (QoS) constraints, aiming to minimize the service cost, which is composed of resource consumption cost, cross-domain operational cost and penalty cost for unsuccessful placement. Then, we prove that the RQSP problem is NP-hard. To solve it, we incorporate GAI technology to devise a novel Generative genetic Algorithm based heuristic SFC Placement (GAP) method. Furthermore, we devise a greedy strategy based population initialization mechanism as well as an elitist and roulette wheel joint selection strategy, to speed up algorithm convergence and reduce runtime overhead. Finally, simulation results demonstrate that compared to benchmark algorithms, the proposed GAP algorithm can achieve better performances on service acceptance ratio, service cost, server utilization and average service delay.