SDN Orchestration Research Articles

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration.

Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

SDN orchestration to combat evolving cyber threats in Internet of Medical Things (IoMT)

Internet of Medical Things (IoMT) is now worth a billion dollar market. While offering enormous benefit, the prevalent and open environment of IoMT ecosystem can be a potential target of varied evolving cyber threats and attacks. Further, extensive connectivity of IoMT devices and their dynamic massive heterogeneous communication can create a new attack surface for sophisticated multivector malware attacks. There is a dire need to protect the forthcoming IoMT industrial revolution from varied evolving cyber threats and attacks. The authors propose a hybrid DL-driven SDN-enabled IoMT framework leveraging Convolutional Neural Network (CNN) and Cuda Deep Neural Network Long Short Term Memory (cuDNNLSTM) for a timely and efficient detection of sophisticated multivector malware botnets. For comprehensive evaluation, a state-of-the-art IoMT dataset and standard performance metrics have been employed. For verification purpose, we compare our proposed framework with our constructed hybrid DL-driven architectures and benchmark algorithms. Our proposed technique outperforms in terms of detection accuracy and testing efficiency. Finally, we also perform 10-fold cross validation to utterly show unbiased results.

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

An SDN-enabled multi-layer protection and restoration mechanism

Transport networks consist of multiple layers, technologies, and areas of deployment. For example, they combine electronic packet and wavelength circuit switching in a single turnkey solution. Conventional solutions have for the longest time segregated the network management and control schemes of these layers, creating silos of operations. This legacy limits flexibility and programmability of extant solutions. In this paper, we describe PROnet, its SDN orchestrator, and a demonstration of its reliability while supporting an advanced manufacturing application running in decentralized mode. PROnet is a two-layer Research and Education Network (REN) under deployed at and around the UT Dallas campus. Resources in the PROnet Ethernet-over-Wavelength Division Multiplexing (WDM) two-layer architecture are managed by a hierarchical SDN orchestrator, which provides two critical functions: 1) on-demand multi-layer path provisioning and 2) coordinated fault handling at both layers. With the first function, the orchestrator automatically provisions optical circuits to efficiently meet the Ethernet flow fault-tolerant requirement as dictated by the application. With the second function, the orchestrator coordinates response procedures at both layers to overcome a first network outage and prepare the network to handle a potential second outage. We refer to this latter function as Multi-layer Protection (implemented at the Ethernet layer) and Restoration (implemented at the WDM layer) mechanism.

A Survey on SDN Orchestration and NFV in Cloud Networking

In recent years cloud computing emerged as a critical tool to enhance competitiveness. Current network infrastructures in cloud data centers limit the ability of cloud provider to offer guaranteed cloud network resources to users. In order to ensure security and performance as defined in the service level agreement (SLA) between cloud user and provider, cloud providers need the ability to provision network resources dynamically and on the fly. In this paper we propose to extend a federated cloud networking security architecture, Network Function Virtualization (NFV) and network orchestrator so that it can secure IoT devices and networks.

Augmented RAN with SDN Orchestration of Multi-tenant Base Stations

Network virtualization (NV) has been successfully applied in wired networks, providing abstraction of the networking equipment and simplifying the network/resource management procedures. However, more light needs to be shed on how the emerging NV technologies, including software defined networking and network function virtualization, can be used by the mobile network operators to efficiently handle the ever-increasing demand for mobile data traffic. In this paper, we focus on the long term evolution system and present a NV-based framework that aims at bringing the access network closer to the end user. The proposed framework enables the dynamic sharing of base stations and radio resources among different mobile network operators, a.k.a. multi-tenant operation. Accordingly, we present novel NV triggering and decision algorithms to demonstrate how the proposed dynamic sharing of base stations can be deployed based on predefined service level agreements. Extensive system-level simulations accompany the paper, showing that, even with the use of simple NV triggering and decision algorithms, the proposed framework results in notable performance improvements at the users of the home operator, i.e. the sending operator, without significantly affecting the performance of the users at the host operator, i.e. the operator that shares its infrastructure.

Demonstration of Adaptive SDN Orchestration: A Real-time Congestion-aware Services Provisioning over OFDM-based 400G OPS and Flexi-WDM OCS

Demonstration of Adaptive SDN Orchestration: A Real-time Congestion-aware Services Provisioning over OFDM-based 400G OPS and Flexi-WDM OCS

Multidomain Network Hypervisor for Abstraction and Control of OpenFlow-Enabled Multitenant Multitechnology Transport Networks [Invited

A virtualization architecture is presented for deploying multitenant virtual networks on top of multitechnology optical networks. A multidomain network hypervisor (MNH) and a multidomain SDN orchestrator (MSO) are introduced with this purpose. TheMNHprovides an abstract view of each virtual network and gives control of it to each independent customer SDN controller. The MNH is able to provide virtual networks across heterogeneous control domains (i.e., generalized multiprotocol label switching and OpenFlow) and transport technologies (i.e., optical packet switching and elastic optical networks). The MSO is responsible for providing the necessary end-toend connectivity. We have designed, implemented, and experimentally evaluated the MNH and MSO in an international testbed across Spain, UK, Germany, and Japan.