Network Traffic Classification using Machine Learning for Software Defined Networks

The recent development in industry automation and connected devices made a huge demand for network resources. Traditional networks are becoming less effective to handle this large number of traffic generated by these technologies. At the same time, Software defined networking (SDN) introduced a programmable and scalable networking solution that enables Machine Learning (ML) applications to automate networks. Issues with traditional methods to classify network traffic and allocate resources can be solved by this SDN solution. Network data gathered by the SDN controller will allow data analytics methods to analyze and apply machine learning models to customize the network management. This paper has focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate the model in software-defined networking platform

NetO-App: A Network Orchestration Application for Centralized Network Management in Small Business Networks

Software-defined networking (SDN) is reshaping the networking paradigm. Previous research shows that SDN has advantages over traditional networks because it separates the control and data plane, leading to greater flexibility through network automation and programmability. Small business networks require flexibility, like service provider networks, to scale, deploy, and self-heal network infrastructure that comprises of cloud operating systems, virtual machines, containers, vendor networking equipment, and virtual network functions (VNFs); however, as SDN evolves in industry, there has been limited research to develop an SDN architecture to fulfill the requirements of small business networks. This research proposes a network architecture that can abstract, orchestrate, and scale configurations based on small business network requirements. Our results show that the proposed architecture provides enhanced network management and operations when combined with the network orchestration application (NetO-App) developed in this research. The NetO-App orchestrates network policies, automates configuration changes, and manages internal and external communication between the campus networking infrastructure.Comment: 12 pages, 4 figures, To appear in the Proceedings of the 4th International Conference on Networks & Communications, 28-29 July 2018, Sydney, Australi

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl

Enhancing Automated Network Management

Network management benefits from automated tools. With the recent advent of software-defined principles, automated tools have been proposed from both industry and academia to fulfill function components in the network management control loop. While automation aims to accommodate the ever increasing network diversity and dynamics with improved reliability and management efficiency, it also brings new concerns as it’s becoming more difficult to understand the control of the network and operators cannot rely on traditional troubleshooting tools. Meanwhile, how to effectively integrate new automation tools with existing legacy networks remains a question. This dissertationpresents efficient methods to address key functionalities within the control loop in the adaption of automated network management.Identifying the network-wide forwarding behaviors of a packet is essential for many network management tasks, including policy enforcement, rule verification, and fault localization. We start by presenting AP Classifier. AP Classifier was developed based on the concept of atomic predicates which can be used to characterize the forwarding behaviors of packets. There is an increasing trend that enterprises outsource their Network Function (NF) processing to a cloud to lower cost and ease management. To avoid threats to the enterprise’s private information, we propose SICS based on AP Classifier, a secure and dynamic NF outsourcing framework. Stateful NFs have become essential parts of modern networks, increasing the complexity in network management. A major step in network automation is to automatically translate high level network intents into low level configurations. To ensure those configurations and the states generated by automation match intents, we present Epinoia, a network intent checker for stateful networks. While the concept of auto-translation sounds promising, operators may not know what intents should be. To close the control loop, we present AutoInfer to automatically infer intents of running networks, which helps operators understand the network runtime states

Special Session on Industry 4.0

No abstract available