Traffic classification plays an essential role in network security and management; therefore, studying traffic in emerging technologies can be useful in many ways. It can lead to troubleshooting problems, prioritizing specific traffic to provide better performance, detecting anomalies at an early stage, etc. In this work, we aim to propose an efficient machine learning method for traffic classification in an SDN/cloud platform. Traffic classification in SDN allows the management of flows by taking the application’s requirements into consideration, which leads to improved QoS. After our tests were implemented in a cloud/SDN environment, the method that we proposed showed that the supervised algorithms used (Naive Bayes, SVM (SMO), Random Forest, C4.5 (J48)) gave promising results of up to 97% when using the studied features and over 95% when using the generated features.

Software-Defined Networking (SDN) and Cloud Computing are now two of the most adopted technologies, on which many organizations are working to enhance every day. For instance, SDN is particularly emerging to solve networking complexity in cloud data centers, so we see many attempts to integrate Network and Cloud Managers. In this paper, we address an integration of these two technologies, particularly a yet undiscussed combination of two popular frameworks: OpenNebula and OpenDaylight. These open source solutions are widely used for cloud management and network management, yet there are no developed modules for communication between the two. Therefore, we propose a simple way for OpenDaylight to manage OpenNebula's compute nodes, using a common component they both support: OpenvSwitch. We compared OpenNebula with the popular OpenStack cloud manager, as it is attracting more attention in both academia and industry, by evaluating some relevant time metrics and discussing the differences of the proposed technologies. Then, we deployed a test topology to conduct some traffic management techniques in this integration. Our results show that OpenNebula's deployment time as well as clean-up time is significantly lower than OpenStack, but OpenStack takes less time to the running state, besides proving the simplicity of traffic management in OpenNebula using OpenDaylight.