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Microservice-based architecture has become prevalent for cloud-nativeapplications. With an increasing number of applications being deployed on cloudplatforms every day leveraging this architecture, more research efforts arerequired to understand how different strategies can be applied to effectivelymanage various cloud resources at scale. A large body of research has deployedautomatic resource allocation algorithms using reactive and proactiveautoscaling policies. However, there is still a gap in the efficiency ofcurrent algorithms in capturing the important features of microservices fromtheir architecture and deployment environment, for example, lack ofconsideration of graphical dependency. To address this challenge, we proposeGraph-PHPA, a graph-based proactive horizontal pod autoscaling strategy forallocating cloud resources to microservices leveraging long short-term memory(LSTM) and graph neural network (GNN) based prediction methods. We evaluate theperformance of Graph-PHPA using the Bookinfo microservices deployed in adedicated testing environment with real-time workloads generated based onrealistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing itwith the rule-based resource allocation scheme in Kubernetes as our baseline.Extensive experiments have been implemented and our results illustrate thesuperiority of our proposed approach in resource savings over the reactiverule-based baseline algorithm in different testing scenarios.

Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN