TY - JOUR
T1 - STCS
T2 - Spatial-temporal collaborative sampling in flow-aware software defined networks
AU - Wang, Xiaofei
AU - Li, Xiuhua
AU - Pack, Sangheon
AU - Han, Zhu
AU - Leung, Victor C.M.
N1 - Funding Information:
Manuscript received October 7, 2019; revised December 22, 2019; accepted January 29, 2020. Date of publication April 8, 2020; date of current version May 21, 2020. This work was supported in part by the National Key Research and Devalopment Program of China under Grant 2019YFB2101901, Grant 2018YFC0809803, and Grant 2018YFF0214700, in part by the China NSFC under Grant 61902044, Grant 61672117, Grant 61702364, and Grant 61529202, in part by the Chongqing Research Program of Basic Research and Frontier Technology under Grant cstc2019jcyj-msxmX0589, in part by the Institute for Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea Government (MSIT) under Grant 2017-0-00195, in part by the U.S. MURI AFOSR MURI under Grant 18RT0073, Grant NSF EARS-1839818, Grant CNS1717454, Grant CNS-1731424, Grant CNS-1702850, and Grant CNS-1646607, in part by the Chinese National Engineering Laboratory for Big Data System Computing Technology, and in part by the Canadian NSERC. This article was presented in part at the IEEE International Conference on Communications (ICC), May 2019, Shanghai, China. (Corresponding author: Xiuhua Li.) Xiaofei Wang is with the Tianjin Key Laboratory of Advanced Networking, School of Computer Science and Technology, Tianjin University, Tianjin 300072, China (e-mail: xiaofeiwang@tju.edu.cn).
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - General traffic analysis based on deep packet inspection (DPI) techniques at switches cannot grasp the detailed knowledge of network applications going into internal switches, and the statistics-based reports of switches lack flow-level recognition of the traffic. Besides, DPI is generally expensive and has limited performance. Therefore, network-wise accurate flow-awareness by packet sampling is highly desirable for fine-grained quality of service guarantee, internal network management, traffic engineering, security analysis, and so on. In this paper, we propose a Spatial-Temporal Collaborative Sampling (STCS) framework in the flow-aware software-defined networks (SDNs). Particularly, considering the spatial-temporal factors and limits of network resources, the formulated STCS problem aims to maximize the network-wise sampling accuracy of flows including mice flows and elephant flows by characterizing both of the comprehensive influences of switches and the effects on sampling accuracy imposed by the collaborative strategy among switches in the spatial-temporal dimension. We propose a suboptimal approach to address the complex STCS problem in two steps: 1) Top-K switch selection based on the iterative comprehensive influence, and 2) sampling time slot allocation based on the local value maximization. Trace-driven evaluation results demonstrate the effectiveness of the proposed framework on improving the sampling accuracy and reducing redundant packets.
AB - General traffic analysis based on deep packet inspection (DPI) techniques at switches cannot grasp the detailed knowledge of network applications going into internal switches, and the statistics-based reports of switches lack flow-level recognition of the traffic. Besides, DPI is generally expensive and has limited performance. Therefore, network-wise accurate flow-awareness by packet sampling is highly desirable for fine-grained quality of service guarantee, internal network management, traffic engineering, security analysis, and so on. In this paper, we propose a Spatial-Temporal Collaborative Sampling (STCS) framework in the flow-aware software-defined networks (SDNs). Particularly, considering the spatial-temporal factors and limits of network resources, the formulated STCS problem aims to maximize the network-wise sampling accuracy of flows including mice flows and elephant flows by characterizing both of the comprehensive influences of switches and the effects on sampling accuracy imposed by the collaborative strategy among switches in the spatial-temporal dimension. We propose a suboptimal approach to address the complex STCS problem in two steps: 1) Top-K switch selection based on the iterative comprehensive influence, and 2) sampling time slot allocation based on the local value maximization. Trace-driven evaluation results demonstrate the effectiveness of the proposed framework on improving the sampling accuracy and reducing redundant packets.
KW - Software-defined networking
KW - flow awareness
KW - redundant packets
KW - sampling accuracy
KW - spatial-temporal collaborative sampling
KW - time complexity
UR - http://www.scopus.com/inward/record.url?scp=85085624241&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2020.2986688
DO - 10.1109/JSAC.2020.2986688
M3 - Article
AN - SCOPUS:85085624241
VL - 38
SP - 999
EP - 1013
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
SN - 0733-8716
IS - 6
M1 - 9061048
ER -