Multiobjective Genetic Algorithm for Fast Service Function Chain Reconfiguration

The optimal placement of virtual network functions (VNFs) improves the overall performance of service function chains (SFCs) and decreases the operational costs for mobile network operators. To cope with changes in demands, VNF instances may be added or removed dynamically, resource allocations may be adjusted, and servers may be consolidated. To maintain an optimal placement of SFCs when conditions change, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, such reconfigurations may lead to stress on the VNF infrastructure, which may cause service degradation. On the other hand, not changing the placement may lead to suboptimal operation, and servers and links may become congested or underutilized, leading to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of their new placement and service-flow routing. We develop a multi-objective genetic algorithm that explores the Pareto front by balancing the optimality of the new placement and the cost to achieve it. Our numerical evaluations show that a small number of reconfigurations can significantly reduce the operational cost of the VNF infrastructure. In contrast, too much reconfiguration may not pay off due to high costs. We believe that our work provides an important tool that helps network providers to plan a good reconfiguration strategy for their service chains.