Extendable NFV-Integrated Control Method Using Reinforcement Learning

Abstract

Network functions virtualization (NFV) enables telecommunications service providers to provide various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services with carrier-grade quality, an NFV controller must optimally allocate such VNFs into physical networks and servers, taking into account combination(s) of objective functions and constraints for each metric defined for each VNF type. The NFV controller should also be extendable, i.e., new metrics should be able to be added. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this algorithm is not extendable because the problem formulation needs to be rebuilt every time, e.g., a new metric is added. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed to optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.