A Network Function Virtualization Resource Allocation Model Based on Heterogeneous Computing

With the continuous increase in the speed and quantity of network traffic, higher performance requirements are put forward for the NFV system. Traditional virtualization technology is limited by slowing down of increase in CPU performance. There has some research on the use of hardware to accelerate network functions. However, existing method only consider use one hardware for acceleration, but hardware itself has limitation. It is difficult to match the network functions and hardware characteristics by considering the combination of each hardware and CPU discretely. In this paper, we propose a resource allocation model of network function virtualization(NFV) based on heterogeneous computing, which can maximize the resource utilization and obtain the global optimal solution. Our experiments prove that the genetic algorithm solution method we propose can take into account both the solution accuracy and the solution speed, and obtain an accurate Pareto curve under the multi-objectives optimization model.