Dynamic resource management for microservices based on deep reinforcement learning

In recent years, the operation and maintenance of Intelligent Computing Center(ICC) have been moving toward the direction of cloud-native, intelligent, and green. Applications deployed in ICC are increasingly adopting containerization technology and microservice architecture. Although the adoption of microservice architecture is expected to simplify the development, deployment, and maintenance of software, the resource competition of microservices can lead to the degradation of application performance and thus affect the quality of user experience. In addition, dependencies between microservices can introduce the cascading effect. These issues make it challenging for operators to achieve efficient resource management while maintaining the quality of user experience.In this paper, we analyze the impact of dependencies between microservices on application performance. We model the service dependency graph as a weighted graph and propose a hybrid resource allocation method based on Deep Q-network. With the goal of maximizing the application performance while minimizing resources usage, our method evaluates the importance of microservices based on the service dependency graph to set the reward function. Using a reinforcement learning algorithm, our method adjusts the compute and network resources allocated to an application based on dynamically changing available resources. Experimental results show that our method can converge quickly and improve the application performance by approximately 68% while reducing the use of compute and network resources.