HGraphScale: Hierarchical Graph Learning for Autoscaling Microservice Applications in Container-based Cloud Computing

TL;DR

HGraphScale is a hierarchical graph neural network-based autoscaling method for microservice applications in cloud environments, effectively capturing dependencies and deployment schemes to improve response times under resource constraints.

Contribution

It introduces a novel hierarchical graph neural network model that captures microservice dependencies and deployment schemes for better autoscaling decisions.

Findings

Reduces average response time by up to 80.16%

Outperforms existing autoscaling methods in experiments

Effective under limited VM rental budgets

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.