SFC-Embedding

across Computing Continuum

version 2-12.2024, used in [3]

Algorithms and simulation tools for Service Function Chain deployment across the Computing Continuum.

Incorporates the following embedding methods

• Hybrid framework.

• Genetic Algorithm.

• Distributed Deep Learning.

• Distributed Greedy algorithm.

A simple way to test these algorithms is to download the whole repository and run the executable .jar files, from console (use jdk-18) without changing the file structure.

Documentation.

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in Data Centers

version 1-08.2023, used in [1,2]

Algorithms and simulation tools for Service Function Chain Embedding in Data Centers.

• Distributed Machine Learning. A Distribute Deep Learning algorithm for SFC-Embedding. [1]

• Genetic Algorithm. A MultiThreaded Genetic Algorithm for SFC-Embedding. [2]

Mored details.

A simple way to test these algorithms is to download the whole repository and run the executable .jar files from console (use jdk-18) without changing the file structure.

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[1] Rodis P. and Papadimitriou P. (2023). "Unsupervised Deep Learning for Distributed Service Function Chain Embedding", IEEE Access, vol. 11, pp. 91660-91672, doi: 10.1109/ACCESS.2023.3308492. pdf

[2] Rodis P. and Papadimitriou P. (2023). "Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding", Journal of Network and Systems Management, 31, 81. doi: 10.1007/s10922-023-09771-y. pdf

[3] Rodis P. and Papadimitriou P. (2024). "Virtual resources provisioning across Computing Continuum", submitted for publication.