Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice architecture from prototype design to large-scale service deployment. To appreciate the big picture of this emerging trend, we provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications. To begin, we identify the key areas in which GNNs are applied, and then we review in detail how GNNs can be designed to address the challenges in specific areas found in the literature. Finally, we outline potential research directions where GNN-based solutions can be further applied. Our research shows the popularity of leveraging convolutional graph neural networks (ConGNNs) for microservice-based applications in the current design of cloud systems and the emerging area of adopting spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) for more advanced studies.
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.
Magnetic field-assisted finishing (MAF) is a surface quality enhancing process that utilizes a flexible brush composed of ferrous metal and abrasive particles. This paper experimentally and statistically investigates the characteristics of a MAF process with nano-scale solid lubricant. A new MAF tool was developed by integrating iron and abrasive particles, and nano-scale solid lubricant. In this experiment, the optical microscopic images of the surface are obtained to measure the surface roughness resulting from MAF processes with varying the content of abrasive particles and the presence of nano-scale solid lubricant. Furthermore, spatial statistics techniques are used to quantitatively evaluate the quality of the surface resulting from each combination of MAF parameters. It is demonstrated that the size and type of abrasive particles mainly affect MAF process and the newly developed MAF tool with nano-scale solid lubricant can improve the final surface quality.
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