Research Area: Software Defined Networks
Software-defined networking (SDN) is one of the most prevailing networking paradigms in current and next-generation networks. Basically, the highly featured separation of control and data planes makes SDN a proper solution towards many practical problems that challenge legacy networks, for example, energy efficiency, dynamic network configuration, agile network measurement, and flexible network deployment. Although the SDN and its applications have been extensively studied for several years, the research of SDN security is still in its infancy. Typically, the SDN suffers from architecture defect and OpenFlow protocol loopholes such as single controller problem, deficiency of communication verification, and network resources constraint. Hence, network measurement is a fundamental technique of protecting SDN against the above security threats. Specifically, network measurement aims to understand and quantify a variety of network behaviors to facilitate network management and monitoring, anomaly detection, network troubleshooting, and the establishment of security mechanisms. In this paper, we present a systematic survey on security-aware measurement technology in SDN. In particular, we first review the basic architecture of SDN and corresponding security challenges. Then, we investigate two performance measurement techniques in SDN, namely, link latency and available bandwidth measurements. After that, we further provide a general overview of topology measurement in SDN including intradomain and interdomain topology discovering techniques. Finally, we list three interesting future directions of security-aware measurement in SDN followed by giving conclusion remarks.
Keywords:
Security
SDN
energy efficiency
dynamic network configuration
agile network measurement
OpenFlow protocol
Author(s) Name: Heng Zhang,Zhiping Cai,Qiang Liu,Qingjun Xiao,Yangyang Li,and Chak Fone Cheang
Journal name: Security and Communication Networks
Conferrence name:
Publisher name: Hindawi
DOI: 10.1155/2018/2459154
Volume Information:
