An Energy-Aware Service Function Chaining and Reconfiguration Algorithm in NFV

With the advances of network function virtualization (NFV) and cloud computing technologies, a number of network services are implemented across data centers by chaining different virtual network functions (VNFs) running on virtual machines. Since these VNFs consume a large amount of resources and power from underlying physical machines, the service function chaining (SFC) algorithm should be carefully designed in order to meet the QoS requirements of users and minimize the power consumptions. This paper proposes an energy-aware SFC algorithm that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is also dynamic in a sense that the SFC path can be reconfigured when the energy consumption of idle servers exceeds a pre-defined threshold of the total energy consumption. We use genetic algorithm (GA) to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other existing studies by as much as 13.4% and reduces both the idle and overall energy consumptions by reconfiguring SFC path.