Joint Deployment and Routing for Hybrid AI Services and Microservices in Edge via Deep Reinforcement Learning

The big data era has accelerated the development of artificial intelligence (AI). The Model-as-a-Service (MaaS) paradigm has been used to address the substantial challenges associated with the organization and development of AI services. However, the successful delivery of complete AI applications is contingent upon the robust collaboration between microservice architectures and AI services. In this case, hybrid orchestration of AI services and microservices is highly necessary, but it still brings challenges. Furthermore, due to the heterogeneity of servers, resource competition, and multi-instance, the difficulty of hybrid orchestration modeling is enlarged. When considering intricate service dependencies among AI services and microservices, the tight coupling of deployment and routing leads to complex joint optimization problems, vastly aggravating the pressure of hybrid orchestration. Nonetheless, extant literature largely failed to address the intricate competitive and collaborative relationships between AI services and microservices, and fine-grained latency analysis with multi-instance modeling in hybrid orchestration problem. Therefore, we study joint deployment and routing for hybrid AI services and microservices in heterogeneous edge. Firstly, we conduct a precise analysis of latency and energy consumption, based on queuing networks and multi-instance models. Secondly, we propose a reinforcement learning method based on potential functions and segmented rewards (PS_SAC) to optimize end-to-end latency and system energy consumption, achieving efficient hybrid orchestration. Finally, through extensive simulation experiments, the algorithm demonstrates significant advantages in reducing latency, improving resource utilization, and lowering system energy consumption.