Predictive VNF Deployment With Virtual Network Mapping Using SDN/NFV-Enabled UAV Swarms

Abstract

Unmanned Aerial Vehicle (UAV) networks are emerging as pivotal enablers for supporting Network Function Virtualization (NFV) and Software-Defined Networking (SDN) services, particularly in meeting the diverse and stringent virtual network function (VNF) scheduling demands of future communication networks. However, a fundamental challenge arises from the SDN controller's inability to synchronize resource request information from VNFs in real time, potentially causing significant delays in mapping and scheduling strategies, especially for delay-sensitive UAV network services. To address this challenge, this paper introduces a predictive VNF deployment model, seamlessly integrated with virtual network mapping, designed to operate within constraints such as the ordered sequence of VNFs, delay requirements, and service arrival time. In recognition of the dynamic nature of UAV services, our framework incorporates VNF live migration and rescheduling. Consequently, we formulate the VNF mapping and scheduling challenge as a predictive long-term lateral resource optimization problem, leveraging Long Short-Term Memory (LSTM) techniques. By employing digital twin (DT)-based virtual network mapping, the SDN controller gains precise insights into the UAV's VNF resource demands, thereby effectively addressing service acceptance issues within VNF mapping and scheduling policies. Our simulation resultsdemonstrate that the proposed method achieves superior outcomes in terms of total benefit, network service acceptance rate, and average delay within the digital twin system. This approach not only enhances the operational efficiency of UAV networks but also ensures robust and timely service delivery in complex network environments, thereby contributing to the advancement of UAV-based NFV and SDN services.