Mobility-Aware Service Function Chain Deployment with Migration in NFV-Based Edge-Cloud

With the development of mobile services such as autonomous driving and the industrial internet, ultralow latency and pervasive mobility have become key characteristics of the intelligent interconnections among people, machines, and things. As a prevailing mobile network architecture, the network function virtualization (NFV)-based edge-cloud architecture brings the computing and memory resources closer to the end user, significantly reducing service delays and supporting more efficient mobility management. However, the geographically distributed nature of the edge-cloud architecture and the quality of service (QoS) requirements of latency-sensitive services in extreme mobile scenarios make service function chain (SFC) deployment more challenging. In this paper, we investigate a mobility-aware SFC deployment scheme with service migration in an NFV-based edge-cloud system. To properly cope with the mobility pattern of mobile services, a multistage decision-making problem is formulated, aiming to jointly minimize the long-term deployment and migration costs and the average end-to-end service latency while simultaneously satisfying various QoS constraints for services and the physical resource constraints of the edge-cloud system. Then, to address the formulated problem, a deep reinforcement learning (DRL)-based online SFC deployment algorithm is proposed that can automatically detect variations in the widely distributed edge-cloud environment and generate online deployment solutions without human intervention to implement adaptive and fast service provision and also support mobile service migration. Extensive experimental results demonstrate our proposed scheme surpasses its competitors in terms of end-to-end latency and migration cost, with average reductions of 6.26% and 18.77%, respectively, while improving the average service acceptance rate by 19.19%.