{"ID":2850993,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01881","arxiv_id":"2511.01881","title":"HGraphScale: Hierarchical Graph Learning for Autoscaling Microservice Applications in Container-based Cloud Computing","abstract":"Microservice architecture has become a dominant paradigm in application development due to its advantages of being lightweight, flexible, and resilient. Deploying microservice applications in the container-based cloud enables fine-grained elastic resource allocation. Autoscaling is an effective approach to dynamically adjust the resource provisioned to containers. However, the intricate microservice dependencies and the deployment scheme of the container-based cloud bring extra challenges of resource scaling. This article proposes a novel autoscaling approach named HGraphScale. In particular, HGraphScale captures microservice dependencies and the deployment scheme by a newly designed hierarchical graph neural network, and makes effective scaling actions for rapidly changing user requests workloads. Extensive experiments based on real-world traces of user requests are conducted to evaluate the effectiveness of HGraphScale. The experiment results show that the HGraphScale outperforms existing state-of-the-art autoscaling approaches by reducing at most 80.16\\% of the average response time under a certain VM rental budget of application providers.","short_abstract":"Microservice architecture has become a dominant paradigm in application development due to its advantages of being lightweight, flexible, and resilient. Deploying microservice applications in the container-based cloud enables fine-grained elastic resource allocation. Autoscaling is an effective approach to dynamically...","url_abs":"https://arxiv.org/abs/2511.01881","url_pdf":"https://arxiv.org/pdf/2511.01881v1","authors":"[\"Zhengxin Fang\",\"Hui Ma\",\"Gang Chen\",\"Rajkumar Buyya\"]","published":"2025-10-23T05:27:29Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[\"Graph Neural Network\"]","has_code":false}