Maximizing Throughput of Delay-Sensitive NFV-Enabled Request Admissions via Virtualized Network Function Placement

Network Function Virtualization (NFV) has attracted significant attention from both industry and academia as an important paradigm change in network service provisioning. In this paper, we explore two different VNF instance scaling techniques: (i) horizontal scaling that migrates some existing VNF instances from their current locations to new locations to allow the VNF instances to be shared by multiple requests to reduce the resource consumption and operational cost of the network; and (ii) vertical scaling that instantiates new VNF instances to meet the demands of new request admissions if sharing existing VNF instances becomes more expensive or the end-to-end delay requirements of currently executing requests will be violated. We first propose a unified framework of maximizing the network throughput by admitting as many NFV-enabled requests as possible while meeting their end-to-end delay requirements, through jointly considering both VNF instance vertical and horizontal scalings. We then provide an exact ILP solution when the problem size is small. Otherwise, we devise an efficient algorithm for the problem through non-trivial reductions. We finally conduct experiments to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms.