Virtual Network Function Placement Considering Resource Optimization and SFC Requests in Cloud Datacenter

• Published in: IEEE transactions on parallel and distributed systems Vol. 29; no. 7; pp. 1664 - 1677
• Main Authors: Li, Defang, Hong, Peilin, Xue, Kaiping, Pei, Jianing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; basic resource consumptions; Cloud computing; Computer simulation; correlation-based algorithm; Data centers; Greedy algorithms; Integer programming; Linear programming; multi-tenancy; Noise measurement; Operation support systems; Optimization; Placement; Resource management; resource optimization; Servers; service function chain; time-varying workloads; Virtual network function placement; Virtual networks; Virtualization
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2018.2802518

Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users' service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.