The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM.

Currently, researchers have motivated a vision of 6G for empowering the new generation of the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G, more computing resources are required, a problem that is dealt with by Mobile Edge Computing (MEC). However, due to the dynamic change of service demands from various locations, the limitation of available computing resources of MEC, and the increase in the number and complexity of IoE services, intelligent resource provisioning for multiple applications is vital. To address this challenging issue, we propose in this paper IScaler, a novel intelligent and proactive IoE resource scaling and service placement solution. IScaler is tailored for MEC and benefits from the new advancements in Deep Reinforcement Learning (DRL). Multiple requirements are considered in the design of IScaler’s Markov Decision Process. These requirements include the prediction of the resource usage of scaled applications, the prediction of available resources by hosting servers, performing combined horizontal and vertical scaling, as well as making service placement decisions. The use of DRL to solve this problem raises several challenges that prevent the realization of IScaler’s full potential, including exploration errors and long learning time. These challenges are tackled by proposing an architecture that embeds an Intelligent Scaling and Placement module (ISP). ISP utilizes IScaler and an optimizer based on heuristics as a bootstrapper and backup. Finally, we use the Google Cluster Usage Trace dataset to perform real-life simulations and illustrate the effectiveness of IScaler’s multi-application autonomous resource provisioning.

Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

The increasing number of Internet of Things (IoT) devices necessitates the need for a more substantial fog computing infrastructure to support the users’ demand for services. In this context, the placement problem consists of selecting fog resources and mapping services to these resources. This problem is particularly challenging due to the dynamic changes in both users’ demand and available fog resources. Existing solutions utilize on-demand fog formation and periodic container placement using heuristics due to the NP-hardness of the problem. Unfortunately, constant updates of services are time consuming in terms of environment setup, especially when required services and available fog nodes are changing. Therefore, due to the need for fast and proactive service updates to meet users’ demand, and the complexity of the container placement problem, we propose in this article a Deep Reinforcement Learning (DRL) solution, named Intelligent Fog and Service Placement (IFSP), to perform instantaneous placement decisions proactively. By proactively, we mean making placement decisions before demands occur. The DRL-based IFSP is developed through a scalable Markov Decision Process (MDP) design. To address the long learning time for DRL to converge, and the high volume of errors needed to explore, we also propose a novel end-to-end architecture utilizing a service scheduler and a bootstrapper. on the cloud. Our scheduler and bootstrapper perform offline learning on users’ demand recorded in server logs. Through experiments and simulations performed on the NASA server logs and Google Cluster Trace datasets, we explore the ability of IFSP to perform efficient placement and overcome the above mentioned DRL limitations. We also show the ability of IFSP to adapt to changes in the environment and improve the Quality of Service (QoS) compared to state-of-the-art-heuristic and DRL solutions.

With the increasing number of IoT devices, fog computing has emerged, providing processing resources at the edge for the tremendous amount of sensed data and IoT computation. The advantage of the fog gets eliminated if it is not present near IoT devices. Fogs nowadays are pre-configured in specific locations with pre-defined services, which limit their diverse availabilities and dynamic service update. In this paper, we address the aforementioned problem by benefiting from the containerization and micro-service technologies to build our on-demand fog framework with the help of the volunteering devices. Our approach overcomes the current limitations by providing available fog devices with the ability to have services deployed on the fly. Volunteering devices form a resource capacity for building the fog computing infrastructure. Moreover, our framework leverages intelligent container placement scheme that produces efficient volunteers’ selection and distribution of services. An Evolutionary Memetic Algorithm (MA) is elaborated to solve our multi-objective container placement optimization problem. Real life and simulated experiments demonstrate various improvements over existing approaches interpreted by the relevance and efficiency of (1) forming volunteering fog devices near users with maximum time availability and shortest distance, and (2) deploying services on the fly on selected fogs with improved QoS.

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art & culture, socialization, commerce, and businesses. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. Moreover, most surveys refrain from providing detailed guidance about the development process of the metaverse, including its impact on technologies, businesses, existing challenges, and potential research directions due to their lack of a macro and micro perception of such a topic. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline and fill the gap in existing Metaverse surveys. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution.