Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers

• Published in: IEEE transactions on parallel and distributed systems Vol. 30; no. 3; pp. 530 - 543
• Main Authors: Tang, Hong, Zhou, Danny, Chen, Duan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Data center networks (DCNs); Data centers; dynamic service function chaining (SFC); Forecasting; Heuristic algorithms; Middleboxes; Networks; Operators (mathematics); Placement; Predictive models; Scaling; Servers; Software; Virtual environments; virtual network functions (VNFs)
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2018.2867587

Traffic in operator networks is time varying. Conventional network functions implemented by black-boxes should satisfy the peak traffic requirement, and hence result in low resource utilization. Thanks to the emergence of Virtual Network Function (VNF), which is realized by running networking software on Virtual Machines (VMs), the operator can dynamically scale in or scale out the VNF instances and hence save the required resources. In this paper, we introduce how the dynamic VNF scaling is implemented in practical operator Data Center Networks (DCNs). First, we analyze the traffic characteristics in our operator networks, and introduce how the VNFs are organized in a common operator DCN. Based on these backgrounds, we not only propose a traffic forecasting method, but also design two VNF placement algorithms to guide the dynamic VNF instance scaling. Through both the implementation in a real operator network and extensive real trace driven simulations, we demonstrate that our dynamic VNF instance scaling system can achieve higher service availability and save the VNF resources (e.g., CPU and memory) by up to 30 percent.