RESCAPE: A Resource Estimation System for Microservices with Graph Neural Network and Profile Engine
Jinghao Wang, Guangzu Wang, Tianyu Wo, Xu Wang, Renyu Yang · 2024
Microservice architecture has become a prevalent paradigm for constructing scalable and flexible cloud-native applications by leveraging the abundant resources of the cloud. However, the topological complexity of microservices poses significant challenges to resource management frameworks that rely on container orchestration. It is paramount to optimize resource utilization within cloud computing clusters while reducing operational costs for service providers. To this end, we present RESCAPE, a framework designed to effectively predict the resource demands of variable microservice workloads. It is instrumental for downstream optimization tasks, particularly heterogeneous resource scheduling, aiming to enhance resource utilization and efficiency. Experiments based on open-source microservice benchmarks such as DeathStarBench and HPC-AI500 demonstrate an average absolute percentage error (MAPE) of 7.9% when forecasting resource needs for the subsequent timestamp, which indicates an adequate precision for resource estimation of microservices.