Machine Learning & Optimization
Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding
This paper introduces GENESIS, a novel Genetic Algorithm-based approach that simultaneously evolves three sine-function-activated Neural Networks to optimally embed Service Function Chains. It addresses the NP-hard problem of Optimal Service Function Chain Embedding (OSE) by tackling chain composition, VNF embedding, and link embedding concurrently. Evaluated across 48 data centre scenarios, GENESIS achieves optimal solutions in 100% of cases and outperforms state-of-the-art Genetic Algorithms and greedy algorithms in both optimality and speed, averaging 15.84 minutes per solution.
Executive Impact: GENESIS at a Glance
This research tackles the complex challenge of optimally embedding Service Function Chains (SFCs) in enterprise networks. The GENESIS algorithm leverages a unique combination of Genetic Algorithms and Neural Networks with sine activation to simultaneously optimize all three critical sub-problems: chain composition, VNF embedding, and link embedding. Our evaluation demonstrates that GENESIS consistently finds optimal solutions, significantly faster than existing methods, ensuring superior resource utilization and network performance for modern data centers.
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Optimal Solution Achieved by GENESIS Across All Scenarios
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Average Convergence Time for GENESIS
Deep Analysis & Enterprise Applications
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100%
Optimal Solution Achieved by GENESIS Across All Scenarios
15.84 mins
Average Convergence Time for GENESIS
GENESIS Workflow for OSE Optimization
Evolve NN Gradients (Genetic Encoding)
→
NN Outputs Heuristic Values
→
Solvers Generate Candidate Sub-solutions (CC, VE, LE)
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Candidate Solution Evaluated on Emulator
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Feedback to Genetic Algorithm
The Optimal Service Function Chain Embedding (OSE) problem is NP-hard and involves simultaneously optimizing three sub-problems: Chain Composition (CC), Virtual Network Function Embedding (VE), and Link Embedding (LE. Traditional approaches often optimize these sequentially or don't cover all three simultaneously, leading to sub-optimal results. GENESIS addresses this by a simultaneous optimization strategy using Neural Networks.
| Algorithm | OSE Sub-problems Optimized | Simultaneous Optimization | Convergence Rate | Average Execution Time |
|---|---|---|---|---|
| GENESIS |
|
Yes | 100% | 15.84 mins |
| BEGA 2000 |
|
No | 71% | 166.64 mins |
| BEGA 100 |
|
No | 21% | 38.62 mins |
| GDA |
|
No | 21% | 41.66 sec |
| GAHA |
|
No | 0% | 709.36 mins |
Innovation in Activation Functions
GENESIS utilizes the sine activation function in its Neural Networks, a departure from conventional functions like ReLU. This design choice significantly improves the exploratory behavior of the genetic algorithm by mitigating the 'dominant gradient problem'. Unlike ReLU, which can lead to certain VNFs consistently appearing first due to higher gradients, the sine function's oscillating output ensures greater diversity in VNF ordering and host embedding, preventing local optima and promoting a more comprehensive search of the solution space. This is a key differentiator for GENESIS's superior performance.
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