Nowadays, Machine Learning (ML) approaches gain a lot of intention for automating and managing Software-Defined Networking, and Network Function Virtualization (SDNNFV) enabled networks. These networks are highly dynamic and flexible due to centralized control and scaleable Virtual Network Functions (VNFs). But the automatic management of the VNF lifecycle in a data center is still challenging; it includes several tasks such as VNF resource usage prediction, placement, consolidation, autoscaling, live migration, etc. So, accurately predicting VNF resource usage can be used for the several tasks mentioned above for performing VNF lifecycle management. It can also help Mobile Network Operators (MNOs) to ensure QoS by reducing Service-Level-Agreement (SLAs) violations. This article introduces an efficient mechanism that uses Adaptive Ensemble Learning to predict resource usage of virtual network functions. This mechanism has three modules: Machine-Learning Predictors (MLPs), Predictor Selector (PS), and Predictor Combiner (PC). The MLPs module contains several ML models for performing prediction. The PS module has a pretrained Random Forest model that is used to choose the best predictors from the MLPs. The PC module combines the selected predictors using an ensemble learning mechanism to generate the final prediction. In tests on three datasets, our method achieved a high${R}^{2}=0.96$for predicting CPU utilization and$R^{2}=0.97$for predicting memory utilization.

5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

Networks slicing in 5G network enables the network operators to accommodate the different quality of service (QoS) to their customers. Moreover, the data analytics in 5G mobile network can be seen as a robust solution to transform the challenging features of 5G into a reality. So, for that, the Third Generation Partnership Project (3GPP) has been introduced a network data analytics function (NWDAF) in 5G service-based architecture (SBA). NWDAF collects the data from different core and management domains and performs analytics on that historical data. It also enables the network operators (NOs) to train their Machine Learning (ML) techniques and use various third-party solutions. On the other side, the automation and management of the end - to-end (e2e) network slicing in a multi -domain environment is a critical task. Therefore, in this manuscript, we have designed a closed-loop Intent-based Networking (IBN) platform, which automatically ensures the commissioning, activation, run-time monitoring, and decommissioning of the network slices. Moreover, we have integrated the newly introduced 3GPP NWDAF with the IBN platform for efficient e2e network slice lifecycle management. By implementing different ML models in the NWDAF function, we can predict the slice load, user mobility, traffic forecasts, and anomaly detection from the network slices.

The expansion of infrastructure and services in the 5th generation networks resulted in complex configuration management throughout the network lifecycle. To this end, network automation replaces existing traditional manual administrative approaches with software-driven repetitive and reliable applications. Since network expansion is in multiple dimensions, including multi-services, domains, and platforms, it is challenging to resolve such a vast infrastructure through a single automation solution. Hence this paper proposed applying an intent-based solution for achieving automatic orchestration for vastly spreading network services. Intent-based solution not only considers network automation but also performs service assurance throughout the network service lifecycle. The proposed IBN (Intent-Based Networking) solution implements a closed-loop network lifecycle management using a single abstracted software platform. It translates high-level requirements to the infrastructure irrespective of the various underlying platforms and domains, and it includes intelligence-driven monitoring and updates for service assurance. A multi-model machine learning approach is proposed in this work to control the network infrastructure reliably. To this end LSTM (Long Short-Term Memory) algorithm is applied for compute-resource prediction and the Route-Net model for optimized service path routing. The infrastructure includes FlexRAN deployed as the access network controller, OSM (OpenSource MANO) resides at the core, and the KOREN network serves as a high-speed transport network.

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

Network slicing is an important pillar of 5G networks that empowers the network operators to provide the different quality of services (QoS) to the users. It enables network operators to split the physical network into multiple logical networks to meet different QoS requirements. In this research paper, we have designed an intent-based network slicing framework that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just needs to provide higher-level information in the form of intents/contracts for a network slice, and in return our system deploys and configures the requested resources. Moreover, a deep learning model Generative Adversarial Neural Network (GAN) has been used for the management of network resources. Several tests have been performed by creating three slices with our system, which shows better performance in terms of bandwidth and latency.

5G claims the service provisioning for a variety of new applications increasing orchestration complexity. Additionally, many different orchestrators and platforms have been developed over recent years to handle and control the network infrastructures. However, each of the orchestration platforms requires different expertise for handling them. The diversity and dynamicity in terms of requirements made it very complex for the operators to handle runtime operations. As with the increasing number of devices, the amount of traffic streamed over network deviates drastically making it impossible for network operators to handle the network operations manually. Hence automated platforms are a dire need, this work proposed a generic intent-based system that can automatically orchestrate and manage network lifecycle over multiple domains, sites and orchestrators. This system will include the orchestration over multiple domains access, transport and core and it also includes the use of Machine Learning for prediction of resource status for proactive decision making to manage the slice lifecycle automatically.

5G networks are aimed at provisioning of a wide range of sophisticated services with uninterrupted user experience. In addition, it is very challenging to manage all the services due the variety in the service requirement and a very large number of users causing dynamic changes in traffic streamed over the network. Currently, the networks are managed manually and requires experts to control the behavior of network. Due to increase in network domain it is an esteem requirement to manage and control network autonomously. In this paper we have introduced and Intent-Based networking approach which on one side abstracts and automates the network configuration also it assures the network resource state stability by using machine learning. We have used an IBN abstraction layer and M-CORD as an next generation testbed for the implementation of this work.

The enormous increase in the number of communicating vehicles every year imposes many challenges for efficient resource allocation in IoV (Internet of Vehicles). Additionally, with advancements, many new communication services have been added to the vehicles, each requiring a unique set of resources. The vehicle communication services include simple video streaming, multimedia, and navigation to mission-critical services in self-driving cars. In addition to that, the vehicular technology is rapidly shifting towards the electric vehicle to help the green energy revolution and reduction of carbon footprints. Similarly, communication services are also consuming high-energy so IoE (Internet of Energy) has also become vital to optimize resource utilization for reduction of energy consumption. Due to the complexity of the service requirements in IoV, an energy-efficient and intelligent resource allocation system is essential. To this end, this paper proposes a solution that provides an efficient and proactive resource orchestration for IoV services while considering edge-cloud infrastructure. Machine Learning (ML) approach has been used to manage the resources by predicting network traffic at the edge, and VNF resource utilization at the core.