Cracking network monitoring in DCNs with SDN

Abstract

The outputs of network monitoring such as traffic matrix and elephant flow identification are essential inputs to many network operations and system designs in DCNs, but most solutions for network monitoring adopt direct measurements or inference alone, which may suffer from either high network overhead or low precision. Different from those approaches, we combine the direct measurements offered by software defined network (SDN) and inference techniques based on network tomography to derive a hybrid network monitoring scheme in this paper; it can strike a balance between measurement overhead and accuracy. Essentially, we use SDN to make the severely low determined network tomography (TM estimation) problem in DCNs to be a more determined one. Thus many classic network tomography algorithms in ISP networks become feasible for DCNs. By combining SDN with network tomography, we can also identify the elephant flows with high precision while occupying very little network resource. According to our experiment results, the accuracy of estimating the TM is far higher than those inferred by SNMP link counters only and the performance of identifying elephant flows is also very promising.