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

Abstract

Microservice-based architecture has become prevalent for cloud-native applications. With an increasing number of applications being deployed on cloud platforms every day leveraging this architecture, more research efforts are required to understand how different strategies can be applied to effectively manage various cloud resources at scale. A large body of research has deployed automatic resource allocation algorithms using reactive and proactive autoscaling policies. However, there is still a gap in the efficiency of current algorithms in capturing the important features of microservices from their architecture and deployment environment, for example, lack of consideration of graphical dependency. To address this challenge, we propose Graph-PHPA, a graph-based proactive horizontal pod autoscaling strategy for allocating cloud resources to microservices leveraging long shortterm memory (LSTM) and graph neural network (GNN) based prediction methods. We evaluate the performance of Graph-PHPA using the Bookinfo microservices deployed in a dedicated testing environment with real-time workloads generated based on realistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing it with the rule-based resource allocation scheme in Kubernetes as our baseline. Extensive experiments have been implemented and our results illustrate the superiority of our proposed approach in resource savings over the reactive rulebased baseline algorithm in different testing scenarios.

An auto-scaling approach for microservices in cloud computing environments

“…Creating the Bayesian graph of relationships between microservices and extracting inference with it can be very useful. So far, few researchers have studied the relationships graph between microservices [34] [37] [38]. Moreover, all of these researches have performed experiments on a small graph with a small number of nodes and few edges which is not necessarily realistic.…”

Fully Decentralized Horizontal Autoscaling for Burst of Load in Fog Computing

“…Previous works propose proactive approaches in horizontally autoscaling microservices, predicting the load of microservices using machine-learningbased methods [9,13,23]. Ming et al go further to create a hybrid of proactive and reactive approaches, handling the decision conflicts between the two approaches to optimizing the elastic expansion [23].…”