NELLY: Flow Detection Using Incremental Learning at the Server Side of SDN-Based Data Centers

摘要

The processing of big data generated by the Industrial Internet of Things (IIoT) calls for the support of processing at the edge of the network, as well as at the cloud data centers. The equal-cost multipath, which is the default routing technique in the cloud data centers, can degrade the network performance when handling mouse and elephant flows. Such degradation of performance can compromise the support of the strict quality of service requirements of the IIoT over 5G networks. Novel techniques for scheduling the elephant flows can alleviate this problem. Recently, several approaches have incorporated machine learning techniques at the controller-side in software-defined data center networks (SDDCNs) to detect elephant flows. However, these approaches can produce heavy traffic overhead, low scalability, low accuracy, and high detection time. This article introduces the Network Elephants Learner and anaLYzer (NELLY), a novel and efficient method for applying incremental learning at the server side of SDDCNs to accurately and timely identify elephant flows with low traffic overhead. Incremental learning enables NELLY to adapt to varying network traffic conditions and perform continuous learning with limited memory resources. NELLY has been extensively evaluated using real traces and various incremental learning algorithms. Results show that NELLY is accurate and supports low classification time when using adaptive decision trees algorithms.