JRLAO: Joint Resource and Latency-Aware SFC Optimized Orchestration in NFV-Enabled Networks
Chenxi Liao, Jia Chen, Deyun Gao, Xu Huang, Shang Liu, Dongsheng Qian · IEEE Transactions on Cognitive Communications and Networking · 2025
Emerging applications such as holographic communications and smart medical care are driving the rapid evolution of future networks. In the network architecture enabled by software defined networking and network function virtualization, service function chaining (SFC), which orderly schedules various virtual network functions (VNFs), is one of the primary methods to provide flexible, agile, and diversified network services. However, these emerging applications face dual challenges of resource allocation and low latency requirements. To address this issue, in this paper, we propose a joint resource and latency-aware based SFC optimization orchestration approach for emerging applications that are delay-sensitive and computation-intensive, establishing a resource-aware scheduling model based on discrete time. The problem is described as an integer linear programming problem with QoS as the optimization objective. Furthermore, we present a deep reinforcement learning based optimal orchestration algorithm for simultaneous VNF embedding and flow routing, the core idea of “joint resource and latency awareness” is applied to realize the scheduling of delay-sensitive services. Finally, the proposed approach is compared with four benchmark algorithms. Experimental results indicate that the proposed approach can enhance QoS, achieve a greater acceptance rate, and complete the scheduling of more delay-sensitive services.