The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. Network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. Network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called Generic Autonomic Network Architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol-level, function-level, node-level, and network-level. In this paper, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed such as SDN, NFV, machine-learning (ML), microservices, multi-agent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

As 6G networks proliferate, they generate vast volumes of data and engage diverse devices, pushing the boundaries of traditional network management techniques. The limitations of these techniques underpin the need for a revolutionary shift towards AI/ML-based frameworks. This paper introduces a transformative approach using our novel Speed-optimized LSTM (SP-LSTM) model, an embodiment of this crucial paradigm shift. We present a proactive strategy integrating predictive analytics and dynamic routing, underpinning efficient resource utilization and optimal network performance. This innovative, two-tiered system combines SP-LSTM networks and Reinforcement Learning (RL) for forecasting and dynamic routing. SP-LSTM models, boasting superior speed, predict potential network congestion, enabling preemptive action, while RL capitalizes on these forecasts to optimize routing and uphold network performance. This cutting-edge framework, driven by continuous learning and adaptation, mirrors the evolving nature of 6G networks, meeting the stringent requirements for ultra-low latency, ultra-reliability, and heterogeneity management. The expedited training and prediction times of SP-LSTM are game-changers, particularly in dynamic network environments where time is of the essence. Our work marks a significant stride towards integrating AI/ML in future network management, highlighting AI/ML's exceptional capacity to outperform conventional algorithms and drive innovative performance in 6G network management.

The advent of network softwarization is enabling multiple innovative solutions through software-defined networking (SDN) and network function virtualization (NFV). Specifically, network softwarization paves the way for autonomic and intelligent networking, which has gained popularity in the research community. Along with the arrival of 5G and beyond, which interconnects billions of devices, the complexity of network management is significantly increasing both investments and operational costs. Autonomic networking is the creation of self-organizing, self-managing, and self-protecting networks, to afford the network management complexes and heterogeneous networks. To achieve full network automation, various aspects of networking need to be addressed. So, this article proposes a novel architecture for the multi-agent-based network automation of the network management system (MANA-NMS). The architecture rely on network function atomization, which defines atomic decision-making units. Such units could represent virtual network functions. These atomic units are autonomous and adaptive. First, the article presents a theoretical discussion of the challenges arisen by automating the decision-making process. Next, the proposed multi-agent system is presented along with its mathematical modeling. Finally, MANA-NMS architecture is mathematically evaluated from functionality, reliability, latency, and resource consumption performance perspectives.

The space industry is growing at a tremendous pace generating attraction both from the industry and academia. Various governmental and industrial institutions are embarking on new programs aiming for more exploration of the industry. The impact of recent advances in the control system, computational technology, networking, Internet of Things (IoT), Robotics, Manufacturing, and Machine Learning (ML)/Artificial Intelligence (AI) could further support the space industry by providing the possibility of detailed and mass exploration of the deeper space. In that regard, this article reviews this multi-discipline area from the space exploration perspective. The article focuses on the most recent advancement in the aforementioned technologies along with control system theory considering the impact of long-distance between the controlling station and the intended site of exploration. We also provided a case-study analysis for the Martian surface while identifying technical and research challenges.

5G and beyond need to meet stringent requirements of latency, reliability, and support for heterogeneous devices. However, the existing wireless network architecture is limited to fulfill these constraints. Cloud radio access network, along with network function virtualization, is suggested to provide flexibility and network agility. It decouples network functions, such as firewall and packet gateway, from hardware to software deployed in the cloud. Thus comprehensive end-to-end formulation of this architecture is required for virtual network function placement. Most of existing works focus on virtual functions placement with different objectives, addressing different service requirements separately. In this article, six 5G constraints are considered simultaneously to find optimal virtual network function placement with service differentiation. The selected six parameters reflect services’ requirements, network constraints and computing constraints. We first model the overall cloud radio access network as a multi-layer loopless-random hypergraph and we provide the overall formulation of the system. Then, we reformulate such model considering backup virtual functions and CPU over-provisioning techniques to improve both virtual function’s reliability and processing latency. Finally, we propose service differentiation to reduce CPU utilization and energy consumption, while using the above techniques. The results suggest that the application of service differentiation can significantly improve assignment of computing resources and energy efficiency.

Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.