标题：An Analytical Model of Data Plane Performance Subject to Prioritized Service
作者：Ren, Yongjian; Jin, Dailiang; Zheng, Dongyu; Liu, Lei; Wei, Xinjing
作者机构：[Ren, Yongjian; Zheng, Dongyu; Liu, Lei] Shandong Univ, Jinan 250101, Peoples R China.; [Jin, Dailiang] Harbin Inst Technol, Harbin 150000, Peoples 更多
会议名称：15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)
会议日期：AUG 23-26, 2016
来源：2016 IEEE TRUSTCOM/BIGDATASE/ISPA
关键词：performance evaluation; self-similarity; SDN; priority queue
摘要：Software Defined Networking (SDN) is emerging as a new paradigm in which the control plane is decoupled from the data plane. SDN is an enabler of network control to become directly programmable. The underlying infrastructure can be abstracted from applications and network services. Recently, SDN has rapidly expanded and attracted many research efforts. In the open literature, analytical tools have been proposed to study the performance of OpenFlow networks to meet the demands of the rapid development of commercial deployment. Jackson model is frequently employed to characterize the features of data plane forwarding. However, network traffic frequently exhibits self-similar characteristic that has been proven repeatedly in traditional networks. Analytical models without taking the self-similar nature into account may lead to unexpected results. This paper establishes a simulation and collects traffic based on Mininet. The Hurst parameter estimation suggests that self-similar nature consists in SDN traffic. To this end, a prioritized service model subject to self-similar traffic flows is developed. A decoupling approach is applied to isolate the interacted traffic flows. Hence, the performance can be obtained by examining a single serve queueing system. Extensive comparisons between the analytical and simulation results suggest the accuracy and feasibility of our work.