Per-Flow Size Measurement by Combining Sketch and Flow Table in Software-Defined Networks

被引:4
|
作者
Qian, Zhaorong [1 ]
Gao, Guoju [1 ]
Du, Yang [1 ]
机构
[1] Soochow Univ, Sch Comp Sci & Technol, Suzhou, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Traffic measurement; SDNs; Sketch;
D O I
10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00088
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In a software-defined network (SDN), traffic statistics from switches are essential for the controller to satisfy different application requirements (e.g., attack detection, load balancing, etc.). To pursue better measurement accuracy, some solutions utilize flow tables to obtain traffic statistics, but the limited TCAM-based flow entries fail to accommodate massive traffic. Another alternative solution is sketch (i.e., a compact data structure), which can be deployed to achieve fine-grained traffic measurement. Nevertheless, traditional sketches (e.g., Count-Min) cannot record flow labels of elephant flows, and meanwhile, sketches that pay excessive attention to the elephant flows inevitably sacrifice the accuracy of the mouse flows. Consequently, this paper proposes a novel model that combines sketch and flow table for per-flow size measurement in SDNs. The sketch in our model not only separates the mouse and elephant flows but also counts the statistics of mouse flows. Moreover, with the designed algorithm, we take full advantage of precious flow entries to keep elephant flows in the flow table. Simulation experiments based on real-world datasets show that our approach has the best performance in per-flow size estimation, flow size distribution, entropy estimation, heavy hitter detection, and heavy change detection compared to existing methods.
引用
收藏
页码:644 / 651
页数:8
相关论文
共 50 条
  • [1] Software-Defined Label Switching: Scalable Per-flow Control in SDN
    Huang, Nanyang
    Li, Qing
    Lin, Dong
    Li, Xiaowen
    Shen, Gengbiao
    Jiang, Yong
    2018 IEEE/ACM 26TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS), 2018,
  • [2] Per-Flow Network Measurement With Distributed Sketch
    Gu, Liyuan
    Tian, Ye
    Chen, Wei
    Wei, Zhongxiang
    Wang, Cenman
    Zhang, Xinming
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2024, 32 (01) : 411 - 426
  • [3] Accurate Per-Flow Measurement with Bloom Sketch
    Zhou, Yang
    Jin, Hao
    Liu, Peng
    Zhang, Haowei
    Yang, Tong
    Li, Xiaoming
    IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2018,
  • [4] TailoredSketch: A Fast and Adaptive Sketch for Efficient Per-Flow Size Measurement
    Gao, Guoju
    Qian, Zhaorong
    Huang, He
    Sun, Yu-E
    Du, Yang
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2025, 12 (01): : 505 - 517
  • [5] AGC Sketch: An effective and accurate per-flow measurement to adapt flow size distribution
    Li, Zhuo
    Liu, Ziyu
    Liu, Jindian
    Zhang, Yu
    Liang, Teng
    Liu, Kaihua
    COMPUTER COMMUNICATIONS, 2024, 221 : 120 - 130
  • [6] Heterogeneous Flow Table Distribution in Software-Defined Networks
    Huang, Jen-Feng
    Chang, Guey-Yun
    Wang, Chun-Feng
    Lin, Chih-Hao
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2016, 4 (02) : 252 - 261
  • [7] Software-Defined Flow Table Pipeline
    Sun, Xiaoye Steven
    Ng, T. S. Eugene
    Wang, Guohui
    2015 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2015), 2015, : 335 - 340
  • [8] Reducing and Balancing Flow Table Entries in Software-Defined Networks
    Jia, Xuya
    Jiang, Yong
    Guo, Zehua
    Wu, Zhenwei
    2016 IEEE 41ST CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN), 2016, : 575 - 578
  • [9] Isolation Guarantees with Flow Table Overflow in Software-Defined Networks
    Chang, Tzu-Wen
    Huang, Zhi-Hong
    Chang, You-Jia
    Kuo, Jian-Jhih
    Tsai, Ming-Jer
    2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2020,
  • [10] JumpFlow: Reducing flow table usage in software-defined networks
    Guo, Zehua
    Xu, Yang
    Cello, Marco
    Zhang, Junjie
    Wang, Zicheng
    Liu, Mingjian
    Chao, H. Jonathan
    COMPUTER NETWORKS, 2015, 92 : 300 - 315