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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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A Survey on Graph Neural Networks for Microservice-Based Cloud Applications.

Hoa Xuan Nguyen1, Shaoshu Zhu1, Mingming Liu1,2

  • 1Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin 9, D09 DX63 Dublin, Ireland.

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Summary

Graph neural networks (GNNs) are revolutionizing cloud-native applications by addressing microservice challenges. This review highlights convolutional GNNs (ConGNNs) and emerging spatio-temporal (STGNNs) and dynamic GNNs (DGNNs) for advanced cloud systems.

Keywords:
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Graph neural networks (GNNs) demonstrate significant success across diverse domains like traffic analysis and computer vision.
  • The rise of cloud-native applications has spurred interest in applying GNNs to microservice architecture challenges.
  • Existing research explores GNNs for microservice design, prototyping, and large-scale deployment.

Purpose of the Study:

  • To provide a comprehensive review of GNN applications in microservice-based systems.
  • To identify key areas where GNNs are utilized within microservice architectures.
  • To outline future research directions for GNN-based solutions in cloud systems.

Main Methods:

  • Systematic literature review of recent studies on GNNs for microservice applications.
  • Categorization of GNN applications based on microservice lifecycle stages (design to deployment).
  • Analysis of GNN design patterns addressing specific microservice challenges.

Main Results:

  • Convolutional graph neural networks (ConGNNs) are currently popular for microservice applications in cloud systems.
  • Spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) represent emerging trends for advanced studies.
  • GNNs offer versatile solutions for various challenges in microservice architecture.

Conclusions:

  • GNNs are a powerful tool for optimizing microservice-based cloud-native applications.
  • ConGNNs are widely adopted, while STGNNs and DGNNs show promise for future advancements.
  • Further research into GNNs can significantly enhance the efficiency and scalability of cloud systems.