AI Service Deployment and Resource Allocation Optimization Based on Human-Like Networking Architecture

Yuqi Hu, Qin Li, Yuhao Chai, Di Wu, Lu Lu, Nanxiang Shi, Yinglei Teng, Yong Zhang

Abstract: In the forthcoming sixth-generation (6G) era, edge-network-cloud collaboration is needed to support artificial intelligence as a service (AIaaS) with a strong demand for computing power. However, how to guarantee the Quality of AI Service (QoAIS) and utilize the edge-network-cloud collaboration to enhance the performance of artificial intelligence (AI) service is a big challenge. In this article, we propose an AI service management and network resource scheduling architecture based on human-like networking. Considering the Quality of Service (QoS) requirements and AI tasks, we propose a joint AI agent placement with deep neural network (DNN) deployment and dynamic bandwidth resource allocation algorithm (JAAPD-D). JAAPD-D is proposed to solve the short-term and long-term joint resource allocation problem which includes communication, computation, and memory resources in the network. We adjust the agent placement, DNN deployment, and schedule routing path to ensure effective service transmission in the long time interval and dynamically allocate bandwidth resources in the short time interval. We use Lyapunov optimization to ensure the system stability of the whole network, meet the QoS requirements of various services, and minimize the average end-to-end delay of services. Simulation results show that JAAPD-D outperforms existing algorithms in terms of delay, traffic accepted rate, network system throughput, and cost.