STCS: Spatial-Temporal Collaborative Sampling in Flow-Aware Software Defined Networks

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...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2020-06, Vol.38 (6), p.999-1013
Main Authors: Wang, Xiaofei, Li, Xiuhua, Pack, Sangheon, Han, Zhu, Leung, Victor C. M.
Format: Article
Language:English
Subjects:
Accuracy
Collaboration
Computer science
Control systems
Electronic mail
flow awareness
Inspection
Iterative methods
Mice
Quality of service
Quality of service architectures
redundant packets
Sampling
sampling accuracy
Security
Slot allocation
Software-defined networking
spatial-temporal collaborative sampling
Switches
time complexity
Traffic control
Traffic engineering
Traffic management
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Summary: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.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.2986688