Network function virtualization has received attention from both academia and industry as an important shift in the deployment of telecommunication networks and services. It is being proposed as a path towards cost efficiency, reduced time-to-markets, and enhanced innovativeness in telecommunication service provisioning. However, efficiently running virtualized services is not trivial as, among other initialization steps, it requires first mapping virtual networks onto physical networks, and thereafter mapping and scheduling virtual functions onto the virtual networks. This paper formulates the online virtual function mapping and scheduling problem and proposes a set of algorithms for solving it. Our main objective is to propose simple algorithms that may be used as a basis for future work in this area. To this end, we propose three greedy algorithms and a tabu search-based heuristic. We carry out evaluations of these algorithms considering parameters such as successful service mappings, total service processing times, revenue, cost etc, under varying network conditions. Simulations show that the tabu search-based algorithm performs only slightly better than the best greedy algorithm.

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.

Recent advances in Artificial Intelligence (AI) on the Internet of Things (IoT) devices have realized Edge AI in several applications by enabling low latency and energy efficiency. However, deploying state-of-the-art Convolutional Neural Networks (CNNs) such as VGG-16 and ResNets on resource-constrained edge devices is practically infeasible due to their extensive parameter counts and floating-point operations (FLOPs). Thus, the concept of network pruning as a type of model compression is gaining attention for accelerating CNNs on low-power devices. State-of-the-art pruning approaches, either structured or unstructured , postulate a three-stage: training-pruning-retraining pipeline, which results in an inevitable retraining overhead. In this work, we posit an orthogonal conjecture on structured pruning at initialization to find sparse subnetworks realizing ≈ 60% less training time than conventional pruning. Moreover, conventional pruning focuses on identifying the saliency at filter granularity while ignoring the importance of characteristics at layer granularity. In contrast, the proposed complexity-driven approach leverages the intrinsic complexities of CNN layers to guide the filter-pruning process without requiring dense pre-training of models. Particularly, we characterize the importance of CNN layers with respect to parameters, FLOPs, and memory-based complexities to work in tandem with filter-pruning in a structured manner. Experiments show the competitive performance of our approach in terms of accuracy and acceleration for all three modes of pruning, namely parameter-aware (PA), FLOPs-aware (FA), and memory-aware (MA). For example, reducing ≈ 70% parameters, ≈ 50% FLOPs, and ≈ 50% memory from MobileNetV2 did not result in any accuracy loss, unlike state-of-the-art approaches. Lastly, we present a trade-off between different resources and accuracy, which can be helpful for developers in making the right decisions in resource-constrained IoT environments.

A new era in telecommunications is emerging. Virtualized networking functions and resources will offer network operators a way to shift the balance of expenditure from capital to operational, opening up networks to new and innovative services. This article introduces the concept of edge as a service (EaaS), a means of harnessing the flexibility of virtualized network functions and resources to enable network operators to break the tightly coupled relationship they have with their infrastructure and enable more effective ways of generating revenue. To achieve this vision, we envisage a virtualized service access interface that can be used to programmatically alter access network functions and resources available to service providers in an elastic fashion. EaaS has many technically and economically difficult challenges that must be addressed before it can become a reality; the main challenges are summarized in this article.

The virtualization of Radio Access Networks (RANs) has been proposed as one of the important use cases of Network Function Virtualization (NFV). In Virtualized Radio Access Networks (VRANs), some functions from a Base Station (BS), such as those which make up the Base Band Unit (BBU), may be implemented in a shared infrastructure located at either a data center or distributed in network nodes. For the latter option, one challenge is in deciding which subset of the available network nodes can be used to host the physical BBU servers (the placement problem), and then to which of the available physical BBUs each Remote Radio Head (RRH) should be assigned (the assignment problem). These two problems constitute what we refer to as the VRAN Placement and Assignment Problem (VRAN-PAP). In this paper, we start by formally defining the VRAN-PAP before formulating it as a Binary Integer Linear Program (BILP) whose objective is to minimize the server and front haul link setup costs as well as the latency (or distance) between each RRH and its assigned BBU. Since the BILP could become computationally intractable, we also propose a greedy approximation for larger instances of the VRAN-PAP.