1. Introduction

This project is based on our accepted paper "Graph Attention LSTM for Load Prediction of Fine-grained VNFs in SFC"

2. Prerequisites

torch==1.11.0+cu115

torch-geometric==2.0.4

scipy==1.9.3

pandas==1.2.4

3. Dataset

• The Public Dataset:

  I. B. Yahia, “Vnfdataset: virtual ip multimedia ip system,” 2019. [Online]. Available: https://www.kaggle.com/datasets/imenbenyahia/cle arwatervnf-virtual-ip-multimedia-ip-system

• The Synthetic Dataset:

  TimeGAN is a framework for generating realistic time series data.

  J. Yoon, D. Jarrett, and M. Van der Schaar, “Time-series generative adversarial networks,” Advances in neural information processing systems, vol. 32, 2019.

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4. GGAL model

• Spatial Learning Module (granularity captured graph attentional layers) + Temporal Learning Module (LSTM) + Prediction Module (MLP)

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