What is it about?
Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. Nevertheless, intelligent orchestration mechanisms are required, such that NFV can exploit its potential and fill up to its promise. In this respect, we investigate the potential gains of embracing Artificial Intelligence (AI) for the virtual network function (VNF) placement problem. To this end, we design and evaluate a genetic algorithm, which seeks efficient embeddings with runtimes on par with heuristic methods. Our proposed embedding method exhibits innovations in terms of network representation and algorithm design, thereby, deviating from typical genetic algorithms. Compared to a heuristic, the proposed genetic algorithm yields higher request acceptance rates, stemming from more efficient resource utilization. We further study a range of factors and parameters that affect the efficiency of the genetic algorithm.
Featured Image
Why is it important?
Network Function Virtualization is the basis of cutting edge technologies in network development.
Perspectives
In contrast to various heuristics and exact methods employed to tackle this problem, we investigated the efficiency of AI-assisted embedding, leveraging on genetic algorithms. Our results indicate that our proposed genetic algorithm confronts the computational complexity of the problem and generates efficient solutions.
Panteleimon Rodis
Hellenic Open University
Read the Original
This page is a summary of: Intelligent Network Service Embedding using Genetic Algorithms, September 2021, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/iscc53001.2021.9631456.
You can read the full text:
Contributors
The following have contributed to this page
