Energy Cost Optimization in Dynamic Placement of Virtualized Network Function Chains

Abstract

Network function virtualization (NFV), with its virtualization technologies, brings cloud computing to networking. Virtualized network functions (VNFs) are chained together to provide the required functionality at runtime on demand. It has a direct impact on power consumption depending on where and how these VNFs are placed and chained to accomplish certain demands as the power consumption of a physical machine (PM) depends on its traffic load. One of the advantages of VNF placement over traditional virtual machine placement is that virtualization is not limited solely to servers. The PMs, including the servers and varying loads to these machines and their utilization, are critical issues related to the network's energy consumption. In this paper, we designed a dynamic energy-saving model with NFV technology using an M/M/c queuing network with the minimum capacity policy where a certain amount of load is required to start the machine, which increases the utilization of the machine and avoids frequent changes of the machines' states. We formulate an energy-cost optimization problem with capacity and delay as constraints. We propose a dynamic placement of VNF chains (DPVC) heuristic solution to the NP-hard problem. The results show that the DPVC solution performs better and saves more energy. It uses 45%-55% less active nodes to satisfy the requested demands and increases the utilization of the active nodes by 40%-50% compared to other algorithms.