Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding

TL;DR

This paper introduces GENESIS, a genetic algorithm that simultaneously evolves neural networks to optimally solve the NP-hard Service Function Chain embedding problem, outperforming existing methods in accuracy and speed.

Contribution

The paper presents a novel genetic algorithm that evolves three neural networks simultaneously to optimize all sub-problems of SFC embedding, achieving superior results.

Findings

GENESIS achieves 100% optimal solutions across all scenarios.

GENESIS is the fastest among tested genetic algorithms.

GENESIS outperforms state-of-the-art algorithms in both accuracy and efficiency.

Abstract

The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise network functions, link them, and programmatically embed them on networking infrastructure. Optimal embedding of Service Function Chains is an NP-hard problem, with three sub-problems, chain composition, virtual network function embedding, and link embedding, that have to be optimised simultaneously, rather than sequentially, for optimal results. Genetic Algorithms have been employed for this, but existing approaches either do not optimise all three sub-problems or do not optimise all three sub-problems simultaneously. We propose a Genetic Algorithm-based approach called GENESIS, which evolves three sine-function-activated Neural Networks, and funnels their output to a Gaussian distribution and an A* algorithm to optimise all three sub-problems simultaneously. We evaluate GENESIS on an emulator across 48 different data centre scenarios and compare its performance to two state-of-the-art Genetic Algorithms and one greedy algorithm. GENESIS produces an optimal solution for 100% of the scenarios, whereas the second-best method optimises only 71% of the scenarios. Moreover, GENESIS is the fastest among all Genetic Algorithms, averaging 15.84 minutes, compared to an average of 38.62 minutes for the second-best Genetic Algorithm.