Joint Deployment and Migration of Service Function Chains for Mobility-Aware Services in an Edge-Cloud Environment

作者：Yuhan Zhang, Ran Wang, J. Hao, Qiang Wu, Zehui Xiong, D. Niyato · 发表于：IEEE Transactions on Cognitive Communications and Networking · 年份：2026 · DOI：10.1109/tccn.2025.3557966 · 被引用次数：11 · 研究领域：Computer Science

With the proliferation of mobile devices, the demand for high-quality mobile services characterized by low latency and high continuity continues to rise. Ensuring the quality of these services is essential to providing intelligent interconnections among individuals, machines, and devices. In the current mobile network landscape, the adoption of network function virtualization (NFV)-based edge-cloud paradigm places network resources in proximity to end-clients, resulting in a substantial reduction in service delays and enhanced efficiency of mobile service management. Nevertheless, the widely decentralized nature of the edge-cloud environment, combined with the stringent quality of service (QoS) demands of delay-sensitive services in dynamic mobile situations, presents a formidable challenge to the deployment of service function chains (SFC). This paper explores a joint deployment and migration of SFCs for mobility-aware services within an NFV-based edge-cloud environment. We formulate a multistage decision-making problem to tackle dynamic mobility patterns of mobile services, aiming to minimize long-term deployment and migration costs, average service latency, while satisfying diverse QoS constraints and adhering to the physical resource constraints of the edge-cloud environment. To tackle the multistage dynamic SFC deployment challenge outlined above, we introduce a deep reinforcement learning (DRL)-based online algorithm. This algorithm autonomously detects variations in th...