Network Management Architecture Research Articles

NAIL: A Network Management Architecture for Deploying Intent into Programmable Switches

NAIL: A Network Management Architecture for Deploying Intent into Programmable Switches

Satellite-Terrestrial Integrated 6G: An Ultra-Dense LEO Networking Management Architecture

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over 20% improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Trust and Performance in Future AI-Enabled, Open, Multi-Vendor Network Management Automation

Cognitive Autonomous Networks (CAN) promise to advance Self Organizing Networks (SON) by applying artificial intelligence to significantly raise the degree of automation in mobile networks. In CAN, Cognitive Functions (CFs) learn the optimal configuration parameter values to optimize specific network metrics, with the execution coordinated via a controller. In open, multi-vendor systems however, the CF’s learning ability may raise a new risk: a manipulative CF (MCF) may learn not only its objective, but also to manipulate the coordination system in pursuit of that objective. In this paper we propose and evaluate our proposed functionality, called CoDeRa, that neutralizes manipulative CF behavior. However, although CoDeRa is effective against MCFs, it is inadequate to resolving error propagation in CF coordination, caused by corrupted network data, for which we have proposed an alternate simpler and cost efficient network management architecture. Our evaluation shows that the proposed design is robust against the observed concerns, and in context of ongoing worldwide standardization efforts, we summarize the relevance and implications of our proposed architecture.

MAGNet: Machine Learning Guided Application-Aware Networking for Data Centers

Modern data centers are witnessing fast-growing east-west traffic on their network infrastructure due to the highly distributed data center applications. Motivated by the heterogeneity of such application workloads, we propose in this paper an extensible network management architecture called MAGNET which enables application-aware intra-data center networking. The crux of MAGNET is the smart endpoint residing within end-hosts, which is empowered by machine learning combined with lightweight workload tracing to detect workload identities and enable workload-dependent packet tagging. The centralized management plane interface of MAGNET allows network functions to interpret packet tags and perform application-aware packet processing. We demonstrate the feasibility of the architecture via prototype implementation and extensive use case evaluation. Our experiments show that the smart endpoint can fingerprint many real-world applications with 99% accuracy only at 12% additional CPU, and that application-aware data plane can potentially bring substantial benefits in terms of security (e.g., via identity-based microsegmentation), CPU usage (e.g., for intrusion detection) and network latency (e.g., via TCP stack customization).

SMART Intent-Driven Network Management

Future communication networks will provide full life cycle network management. However, the complexity of the network management should be reduced even without the requirements of the technical knowledge. Meanwhile, the network expects to be managed in a fully automated manner with humans out of the loop. The emerging intent-driven network (IDN) provides a structured method of specifying the management intent, which is automatically translated and deployed into a network device configuration. In this article, we first introduce the motivations of the IDN and provide an overview of the IDN in various fields. From the perspectives of network systems and user perception, we illustrate the five levels of achieving full autonomous network management. Then we propose an intent-driven network management architecture. In particular, we focus on a specific kind of network management intent, which can be measurable and achievable, leading to rewarding and time-bound full life cycle management, called SMART. Moreover, we give technical details of the SMART architecture. Finally, we develop an intent platform based on OpenStack and present the intent system demonstration interface.

Deployment of Inductive Loop Vehicle Traffic Counters Along Trunk Roads in Tanzania

As Tanzania's population grows, there is a greater demand for efficient, effective, and strong road design, as well as a smooth transportation system with a balanced traffic management network architecture. To plan for maintenance and expansion of such a vast network, a credible data inventory and network condition must be established. The deployment of vehicle inductor loop counters along Tanzanian trunk roads is presented in this paper. At test sites, inductor loop sensors and control cards were used to capture vehicle traffic data, which was then wirelessly sent using GSM technology for vehicle classification verification and storage for future road traffic management. The traffic count statistics from selected test sites acquired by the base station monitoring software were examined in real-time and the findings over a one-year period were provided. According to the study's findings, inductive loop vehicle counter technology can count and categorize vehicles along a section of roadway and provide reliable performance indicators for monitoring progress at the local, state, and national levels. The vehicle traffic data collected by this system will be used to project future situations within a transportation system and to keep a record of historical trends that will be used to project into the future what is likely to happen based on actual observations of the past and using other socio-economic data obtained from census information or economic indicators.

Hierarchical Domain-Based Multicontroller Deployment Strategy in SDN-Enabled Space–Air–Ground Integrated Network

The space–air–ground integrated network (SAGIN) is considered to be a significant framework for realizing the vision of “6G intelligent connection of all things.” A typical SAGIN consists of three parts: a space-based network composed of various orbiting satellites, an air-based network composed of aircraft, and a traditional ground-based network. Considering the cost of satellite launch, the network needs to be flexible and controllable. In order to ensure that the ground can handle satellite anomalies in real time by program, it is necessary to introduce in-orbit programmable networks, such as the software-defined network (SDN). In the network management architecture, if the controller plane in the SDN adopts the flat management scheme, the expansion of the control plane is limited due to the low efficiency of data synchronization among controllers. Compared with controller deployments on terrestrial networks, multicontroller deployments in the SAGIN face the following problems: the dynamic change of the satellite network topology, the large-scale network nodes, the increase or decrease in the number of aerial vehicles, and the unbalanced distribution of ground users. Therefore, it is of great significance to study how to optimize the deployment of multiple controllers in the SDN-enabled SAGIN. This article introduces an SDN into the SAGIN and designs a hierarchical domain-based SDN-enabled SAGIN architecture. A multicontroller deployment strategy for the hierarchical domain-based SDN-enabled SAGIN is proposed. First, we divide the SDN control plane into two layers, i.e., the primary controller layer is deployed on the ground network and the secondary on the space-based network. The SDN data plane is composed of space-based, air-based, and ground-based networks. Second, considering the average network delay and the controller load, a multiobjective optimization model is constructed. To determine the number of controllers and the relative positions of switch nodes and controllers, the clustering algorithm based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means is adopted to initially divide the data plane. Finally, to improve the global search ability of the algorithm, a multiobjective optimization algorithm based on a genetic algorithm is adopted. The simulation results show that the proposed strategy is effective in reducing the average network delay and improving the controller load balance. Compared to other algorithms, the average network delay is reduced by 13.3% and the controller load is improved by 10.33%.

An Intelligent Blockchain-Assisted Cooperative Framework for Industry 4.0 Service Management

The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.

A Novel Machine Learning-Based Scheme for Spectrum Sharing in Virtualized 5G Networks

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

A Software Defined Networking-based Routing Algorithm for Flying Ad Hoc Networks

Flying Ad-Hoc Networks (FANET) are wireless mobile ad-hoc networks composed of unmanned aerial vehicles (UAV) as communicating nodes. As with any computer network, routing is an essential problem that has to be solved efficiently for high performance. FANETs present unique challenges with respect to routing, due to their structures. FANET systems have high dynamicity as the nodes move at very high speeds and UAVs can behave in accordance with various mobility models. The nodes usually have line of sight between them, but FANET systems frequently operate on large topologies with low node density. Hence, the structure of the topology changes rapidly, and the frequency of link disconnections between UAVs increases. Traditional topology-based and position-based routing algorithms do not work well in the face of this problem. In this study, we propose a novel SDN-based Routing Protocol which comprises both proactive and reactive components in order to improve the performance. Software Defined Networking technology is used as the network management architecture. To investigate the performance of the proposed protocol against legacy MANET routing protocols, a comparison study was conducted in terms of throughput, end-to-end delay, and control packet overhead. Simulation results show that the proposed SDN-based Routing Protocol performs better than the selected legacy protocols.

An Optimal SDN-Based Wavelength Allocation and Routing Method for 5G Network

Optical networks are changing as new advanced technologies emerge. With each passing year, their sizes and capabilities expand. The standard architecture for network control and management cannot handle all of these complexities. The proliferation of cloud services and the massive volume of traffic provided by content delivery networks are driving the present fast increase in Internet traffic. This obviously exacerbates congestion concerns in communication networks, with a focus on the core and backbone components in particular. Software-defined networking (SDN) is evolving into a consolidated network management system that comprises a variety of strategies aiming at network management that are based primarily on one basic principle: decoupling control plane decisions from data plane activities. An essential resource allocation strategy in an all-optical network is routing and wavelength assignment. A novel SDN-based approach is proposed to address the problem of old methods mixed with new architecture in optical networks. The network resources were optimized for optimal scheduling using a binary hybrid topology particle swarm optimization method. In terms of recovery time, blockage rate, and resource consumption, simulation results demonstrate that the suggested technique outperforms previous classical methods.

Optical access evolutions towards SDN and disaggregated hardware: an operator perspective [Invited

In this paper, we review recent advances of the optical access network in the path toward Cloud-computing-related technologies, software defined networking, and network function virtualization. We present the new network management architectures proposed by standardization bodies such as the Broadband Forum and non-profit organizations such as the Open Networking Foundation and propose to use today’s Orange-France Operation Support System as a baseline to discuss different evolution paths. We also provide a review of recent advances in the optical line terminal (OLT) hardware evolution, from white-box OLTs to general purpose hardware, and identify the remaining related challenges, such as energy consumption and interoperability. We also show how Cloud-computing technologies in optical access networks could remove the frontier between network segments (home local area network, optical transport network, and mobile network) by deeply improving the synergy among them.

Design of Power Communication Data Network Management System and Implementation of Data Acquisition Module

The power communication network is a separate network from the power grid whose primary purpose is to ensure the power grid's safe operation. This paper expounds the composition of the comprehensive network management architecture of the power communication data network and the implementation of the data acquisition module in the network management system through theoretical analysis, for the reference of relevant personnel, in order to better promote the collection of power grid communication network data.

Construction of Wireless Underground Footwork Mobile Training and Monitoring Sensor Network in Venues of Major Sports Events

At the Summer Olympics in Tokyo, technology was used extensively in major sports events. The level of foot movement ability greatly affects the performance of sports technology. Modern sports are developing in the direction of high speed, high skills, flexibility, and rapidity, and more and more reflect the important position of reasonable and accurate foot movement ability in sports. This article uses wireless sensor technology and wireless communication technology to design the overall architecture of the wireless underground footwork mobile training and monitoring network in venues of major sports events. According to the determined monitoring parameters and data transmission plan, a wireless remote monitoring data acquisition system is designed, and the hardware design, software design, and networking of the wireless monitoring node are completed, so as to realize the real-time monitoring and remote transmission of the underlying data. This paper proposes a wireless sensor network management architecture and method based on multiagent cooperation and combines active and passive wireless underground footwork mobile training and monitoring for experimental verification. A multitask allocation strategy optimized for network working life is proposed. A genetic algorithm is used to model and optimize the task data report routing of cluster head nodes. The simulation experiment results show that the wireless sensor network management method based on multiagent cooperation can effectively coordinate different monitoring sensor nodes to complete the assigned monitoring tasks; the multitask assignment strategy based on a genetic algorithm can optimize the working life of the application network.

Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

The Software-Defined Network (SDN) is a new network paradigm that promises more dynamic and efficiently manageable network architecture for new-generation networks. With its programmable central controller approach, network operators can easily manage and control the whole network. However, at the same time, due to its centralized structure, it is the target of many attack vectors. Distributed Denial of Service (DDoS) attacks are the most effective attack vector to the SDN. The purpose of this study is to classify the SDN traffic as normal or attack traffic using machine learning algorithms equipped with Neighbourhood Component Analysis (NCA). We handle a public “DDoS attack SDN Dataset” including a total of 23 features. The dataset consists of Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and Internet Control Message Protocol (ICMP) normal and attack traffics. The dataset, including more than 100 thousand recordings, has statistical features such as byte_count, duration_sec, packet rate, and packet per flow, except for features that define source and target machines. We use the NCA algorithm to reveal the most relevant features by feature selection and perform an effective classification. After preprocessing and feature selection stages, the obtained dataset was classified by k-Nearest Neighbor (kNN), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms. The experimental results show that DT has a better accuracy rate than the other algorithms with 100% classification achievement.

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.

An Energy-Efficient Unselfish Spectrum Leasing Scheme for Cognitive Radio Networks.

Cooperative Communications in Cognitive Radio (CR) have been introduced as an essential and efficient technique to improve the transmission performance of primary users and offer transmission opportunities for secondary users. In a typical multiuser Cooperative Communication in CR, each primary user can choose one secondary user as a relay node. To encourage the cooperative behavior of the secondary users, primary users lease a fraction of their allocated spectrum to the relay secondary users to transmit their data packets. In this work, a novel unselfish spectrum leasing scheme in CR networks is proposed that offers an energy-efficient solution minimizing the environmental impact of our network. A network management architecture is introduced, and resource allocation is proposed as a constrained sum energy efficiency maximization problem. The optimization problem is formulated and solved using non-linear programming methods and based on a modified Kuhn-Munkres bipartite matching algorithm. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.

From Design to Practice: ETSI ENI Reference Architecture and Instantiation for Network Management and Orchestration Using Artificial Intelligence

The European Telecommunications Standards Institute launched the Industry Specification Group (ISG) on Experiential Networked Intelligence (ENI) in February 2017. The work carried out in ENI in the first year was summarized in [1]. In this article, we give a comprehensive review of the recent developments of ISG ENI in the second year, focusing on the architecture, reference points, and their categorization and applications to specific use cases, as well as proofs of concepts developed in ENI on intelligent network operation and management using artificial intelligence.

A taxonomy of blockchain-enabled softwarization for secure UAV network

The recent advancements in unmanned aerial vehicles (UAVs) upsurges its usages in commercial and civilian applications such as surveillance, rescue, and crowdsensing. UAVs are vulnerable to being destroyed, lost, or stolen in case of security breaches of its network. The network management of UAVs is a crucial task due to its high mobility, which necessitates UAV network softwarization. Then, it becomes indispensable that allows the separation of control functions (i.e., control plane data) from hardware for smooth execution of complex operations. Further, UAV uses the Internet (an open channel) for communication in its complex system that raises a network security concern. The well-known softwarization techniques, i.e., software-defined networking (SDN) and network function virtualization (NFV) can be used to accomplish the secure network services on less expense. Conversely, these softwarization techniques may suffer from various threats like access control, user authentication, controller hijacking, and many more attack. The existing solutions are using a centralized system, which is having a single point of failure issue and vulnerable to security threats. Motivated from these facts, we present a comprehensive and systematic survey on the blockchain-based softwarization for a secure UAV network. Then, we propose a blockchain-enabled UAV softwarization architecture for secure communication and network management. It provides dynamic, flexible, and on-the-fly decision capabilities for communication services over the UAV network. Eventually, we analyzed the open research issues and challenges for future research directions in this emerging area.