OpenFlow-enabled user traffic profiling in campus software defined networks

The recently emerging paradigm of software defined networking (SDN) predominantly employs the control-data plane OpenFlow protocol, offering centralized real-time programmability and monitoring of network devices. Effective SDN based traffic engineering using OpenFlow, particularly in campus networking however, requires sophisticated real-time user traffic visualization solution having minimum management overhead. To address the intuitive monitoring gaps in existing campus based SDN, the present paper proposes profiling campus user traffic to visualize real-time network workload and accurately provision resources. The design solely utilizes existing OpenFlow traffic measurements, subjected to k-means clustering to segregate users into different traffic classes (profiles) based on their application trends. To validate design feasibility, the present study derived six unique user traffic profiles from OpenFlow generated traffic statistics of a realistic campus switch over a two-week time frame. The derived profiles represented significant discrimination among user application trends and were further benchmarked for high stability (96.1-99.1%), to ascertain their viability for monitoring purposes. Additional simulation tests at varying user loads attributed minimum computational cost and low OpenFlow control overhead (4.02-4.96%) to the proposed approach, offering high scalability for real-time network monitoring and resource provisioning.