This repository provides the implementation of HGMSD, a hypergraph-based microservice deployment framework designed for multi-cloud environments. HGMSD models service dependencies, resource states, and network topology as a unified hypergraph, and optimizes service deployment via deep reinforcement learning to reduce image pull latency and cross-subnet communication overhead.

• Python 3.7.6
• TensorFlow 2.8.0
• Numpy 1.19.5

Install dependencies:

pip install tensorflow==2.8.0 numpy==1.19.5

To train HGMSD:

python hypergraph_main.py

To evaluate HGMSD with image pull latency:

python hypergraph_pull_main.py

Baselines can be run via:

python RMS_DDPG_main.py
python RMS_pull_main.py
python rsdql_main_pull.py

Key performance metrics:

• Response Delay
• Image Pull Delay
• Total Delay

The experimental results are visualized in the figure/ folder after execution.

We plan to enhance the generalization and practical applicability of the framework by formalizing all modeling assumptions and defining quantitative benchmarks.

This project is for academic research purposes only. For other usage, please contact the authors.