In this work we outline a framework for measurement-based performance evaluation in SDN environments. The SDN paradigm, which is based on a strict separation of the network logic from the underlying physical substrate, necessitates a comprehensive global view of the network state. To augment the network representation, we propose mechanisms for extracting traffic characteristics from network observations which are used to derive performance metrics. Such metrics can be exploited by SDN applications to optimize the performance of SDN services. Given the bursty nature of network traffic and the well known adverse impact of this property on network performance, we propose an approach for extracting flow autocorrelations from switch counters. Our main contribution is a random sampling approach that reduces the monitoring overhead while enabling a fine grained characterization of the flow autocorrelation structure. We analytically evaluate the impact of random sampling and demonstrate how services may use the estimated traffic properties to compute useful performance metrics.
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