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http://dx.doi.org/10.9717/kmms.2015.18.2.189

Large Flows Detection, Marking, and Mitigation based on sFlow Standard in SDN  

Afaq, Muhammad (Dept. of Computer Eng., Jeju National University)
Rehman, Shafqat (Dept. of Comp. Sci. & Eng., Air University)
Song, Wang-Cheol (Dept. of Computer Eng., Jeju National University)
Publication Information
Abstract
Despite the fact that traffic engineering techniques have been comprehensively utilized in the past to enhance the performance of communication networks, the distinctive characteristics of Software Defined Networking (SDN) demand new traffic engineering techniques for better traffic control and management. Considering the behavior of traffic, large flows normally carry out transfers of large blocks of data and are naturally packet latency insensitive. However, small flows are often latency-sensitive. Without intelligent traffic engineering, these small flows may be blocked in the same queue behind megabytes of file transfer traffic. So it is very important to identify large flows for different applications. In the scope of this paper, we present an approach to detect large flows in real-time without even a short delay. After the detection of large flows, the next problem is how to control these large flows effectively and prevent network jam. In order to address this issue, we propose an approach in which when the controller is enabled, the large flow is mitigated the moment it hits the predefined threshold value in the control application. This real-time detection, marking, and controlling of large flows will assure an optimize usage of an overall network.
Keywords
Large Flows; Small Flows; sFlow; OpenFlow; Priority Marking; SDN; DDoS;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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