Abstract: The limited resources of edge devices make it necessary to deeply understand the resource consumption of network functions to deploy edge services. Through the deployment experiment of containerized network functions on wireless routers, it is concluded that in addition to the computational overhead of processing business flows, communication between network function instances will also consume a lot of CPU resources. Based on this observation, the distributed deployment of network function instances on peer-to-peer edge devices at close range and relatively low load is considered, and the traffic under the condition of satisfying delay constraints are balanced, thereby minimizing the edge device load. Therefore, a fine-grained service chain load model is proposed, and on this basis, a multi-instance service chain online deployment algorithm for edge environments is designed and implemented. The algorithm includes three components: the delay satisfaction path search based on the pruning search strategy, the deployment path selection based on the nested Top K strategy, and the network function deployment based on the greedy strategy. Simulation experiments verify the effectiveness of the algorithm. The experimental results show that compared to the deployment of network function chains without considering communication overhead, the algorithm proposed in this paper can reduce the CPU load of edge devices by 10%, which is close to the theoretical optimal deployment result.

Key words: edge computing, network function virtualization, service chain deployment