1 Introduction
2 Related works
3 The description of network model and performance metrics
3.1 The introduction of network model
3.2 The performance metrics
3.2.1 Revenue
3.2.2 Energy cost
4 Proposed solution
4.1 Particle swarm optimization basics
4.2 Discrete PSO for VNE problems
4.3 Aggregation strategy for fitness function
4.4 Niche PSO
4.5 Description of niche PSO
5 Performance evaluation
5.1 Evaluation settings
Notation | Algorithm description |
---|---|
MO-NPSO | The approach of multi-objective and meta-heuristic VNE which based on particle swarm optimization, aiming at both maximizing the revenues and minimizing the energy consumption. It adopts the adaptive weighted strategy. |
EA-PSO | An energy-aware VN embedding algorithm proposed in our previous work [14]. It is also based on particle swarm optimization. |
D-ViNE-SP | The single-objective VN embedding algorithm proposed in [12], aiming at maximizing the revenues by optimizing the resource cost. Take the shortest path algorithm to determine the link mapping solution after node mapping stage. |
5.2 Experimental results
Algorithm | Running time |
---|---|
D-ViNE-SP | 4 s |
EA-PSO | 100 ms |
MO-NPSO | 110 ms |