Topology-Aware Self-Adaptive Resource Provisioning for Microservices

Microservice architecture is a popular technology for deploying services in cloud computing, with benefits like loose coupling, high fault tolerance, and scalability. The heterogeneous resource requirements and complex interaction relations have increased the difficulty in provisioning resources for microservices with intricacy topology. Existing approaches allocate resources for different microservices separately, and thus cannot achieve optimal global performance. Moreover, these approaches extract features from specific microservice topologies. We propose a topology-aware self-adaptive resource provisioning approach for microservices. Firstly, we propose a microservice state graph to characterize the status of each microservice in an application. Then, we use graph neural networks and attention to extract the resource requirements and correlation features of microservices. Thirdly, we use a reinforcement learning-based approach to allocate resources for microservices uniformly. Finally, we evaluate our approach by conducting a series of experiments on three typical microservice applications deployed in a heterogeneous cluster. The results show that our approach is efficient in extracting resource and correlation features of microservices, and can guarantee QoS with efficient resource utilization. Our approach can reduce the End-to-End latency by 22%, and can improve resource utilization by 18% with guaranteed latency.