Performance Analysis of Software-Defined Network Switch Using M / Geo / 1 Model

被引:42
|
作者
Sood, Keshav [1 ]
Yu, Shui [1 ]
Xiang, Yong [1 ]
机构
[1] Deakin Univ, Sch Informat Technol, Geelong, Vic 3216, Australia
关键词
Performance evaluation; SDN switch modeling;
D O I
10.1109/LCOMM.2016.2608894
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The aim of this letter is to propose an analytical model to study the performance of software-defined network (SDN) switches. Here, SDN switch performance is defined as the time that an SDN switch needs to process packet without the interaction of controller. We exploit the capabilities of queueing theory-based M / Geo / 1 model to analyze the key factors, flow-table size, packet arrival rate, number of rules, and position of rules. The analytical model is validated using extensive simulations. This letter reveals that these factors have significant influence on the performance of an SDN switch.
引用
收藏
页码:2522 / 2525
页数:4
相关论文
共 50 条
  • [21] Performance and Security Oriented Software-Defined Network Interface Design
    Huang, Ken-Shin
    Chao, Hung-Lin
    Wu, Tsung-Tien
    Hsiung, Pao-Ann
    2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2017,
  • [22] Performance of Software-Defined Networking Controllers for Different Network Topologies
    Alrashedy, Kamel
    Kimmett, Ben
    Gulliver, T. Aaron
    2017 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING (PACRIM), 2017,
  • [23] Towards Analysis of the Performance of IDSs in Software-Defined Networks
    Niknami, Nadia
    Inkrott, Emily
    Wu, Jie
    2022 IEEE 19TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2022), 2022, : 787 - 793
  • [24] On SDPN: Integrating the Software-Defined Perimeter (SDP) and the Software-Defined Network (SDN) Paradigms
    Lefebvre, Michael
    Engels, Daniel W.
    Nair, Suku
    2022 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS), 2022, : 353 - 358
  • [25] An approach to enhance packet classification performance of software-defined network using deep learning
    Indira, B.
    Valarmathi, K.
    Devaraj, D.
    SOFT COMPUTING, 2019, 23 (18) : 8609 - 8619
  • [26] Simulation of Network Migration to Software-Defined Network
    Rahim, Mukti
    Hikmatullah, Muhammad Rizky
    Saskara, GedeArna Jude
    Rachmana, Nana S.
    2015 9TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATION SYSTEMS SERVICES AND APPLICATIONS (TSSA), 2015,
  • [27] Performance evaluation of Software-Defined Network (SDN) controllers using Dijkstra's algorithm
    Zhang, Yinjun
    Chen, Mengji
    WIRELESS NETWORKS, 2022, 28 (8) : 3787 - 3800
  • [28] Performance evaluation of Software-Defined Network (SDN) controllers using Dijkstra’s algorithm
    Yinjun Zhang
    Mengji Chen
    Wireless Networks, 2022, 28 : 3787 - 3800
  • [29] Prediction and detection model for hierarchical Software-Defined Vehicular Network
    Amari, Houda
    Khoukhi, Lyes
    Belguith, Lamia Hadrich
    PROCEEDINGS OF THE 2022 47TH IEEE CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2022), 2022, : 463 - 470
  • [30] Analytical Model for OpenFlow-Based Software-Defined Network
    Sarkar, Chiranjit
    Setua, S. K.
    PROGRESS IN COMPUTING, ANALYTICS AND NETWORKING, ICCAN 2017, 2018, 710 : 583 - 592