Extended reality (XR) services form the basis of various innovative applications. These new applications are expected to run on mobile devices with limited computational and energy capabilities. At the same time, XR services should fulfill some expectations regarding data rates and end-to-end latency to guarantee an uninterrupted user experience. In this context, offloading intensive computation to the edge is particularly advantageous for mobile devices, enabling access to more capable processing hardware. Current studies on computational offloading focus mainly on the compute and network continuum formed between edge and cloud and realized mostly through containers. Conversely, this demonstration showcases a computational offloading framework that allows the expansion of XR services functionality from the connected mobile device to an edge computing environment. The presented framework is based on a portable, lightweight, and secure WebAssembly runtime and uses open technology implementations. We demonstrate that the developed framework allows significant performance improvements in a Yolo object detection application and reduces heat generation on mobile devices.

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

The virtualized radio access network (vRAN) placement problem can be defined as the joint decision of choosing the functional splits of the radio stack, where to run the virtualized functions of vRAN nodes, and the paths connecting the base stations with their respective protocol stacks. This optimization problem has been widely investigated in the literature with exact and heuristic approaches. While exact approaches still present very limited scalability, heuristic approaches achieve results still notably far from optimal. Metaheuristic techniques tend to be successful in this context, and an evolutionary approach has already shown promising results in a simplified version of the problem. In this work, we also employ a genetic algorithm to solve the vRAN placement problem but use a flexible formulation of the vRAN placement problem. We compare our proposal with two exact approaches and one heuristic approach (based on machine learning) from the literature. Our proposal is able to solve large instances of the problem in a reasonable time while achieving satisfactory results, close to the optimal. Additionally, with our knowledge of the problem, we created synthetically a single individual in the first generation which made it possible to obtain a high-quality (i.e., close to the optimal) first solution for several instances.

In this work, we investigate how to minimize the number of gateways deployed in Unmanned Aerial Vehicles (UAVs) needed to meet the demand for non-3GPP Internet of Things (IoT) devices, seeking to improve the Quality of Service (QoS), keeping a balance between delay and data rate. Gateways deployed in UAVs add the mobility flexibility of UAVs, which paves the way for meeting emergency demand increments. The 5thGeneration Networks (5G) and Beyond 5thGeneration Networks (B5G) systems incorporated access to IoT devices via non-3GPP access, opening up new integration possibilities. Furthermore, Low Power Wide Area Network (LPWAN) networks, especially Long Range Wide Area Network (LoRaWAN), allow access over long distances with reduced energy consumption. In this scenario, our work proposes a Mixed Integer Linear Programming (MILP) optimization model to minimize the number of UAVs that meet the increment of emergency demand, comply with limits of QoS, and maintain the compromise between delay and data rate. Simulation results show that the proposed model significantly reduces the number of gateways, maintains optimal levels of QoS, and maintains the compromise between delay and data rate compared to the presented baselines.

The coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user quality of experience (QoE). However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user QoE and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE under the constraints of UE capabilities and the availability of network resources. In order to make the problem manageable, we assume that the regular user consumes video content keeping some time interval between them, and this user moves slowly inside the coverage of a base station. We evaluate our proposal using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and cache hit ratio by at least 14% in comparison with the other method.

Openness, virtualization and disaggregation of functions represent the state-of-the-art (SOTA) for optimal management and orchestration of Radio Access Network (RAN) resources. However, in 5G and beyond networks, virtualized RAN (vRAN) functions may commonly share computing resources with Multi-access Edge Computing (MEC) services. This paper introduces a new problem formulation that jointly optimizes vRAN functions and MEC services respecting the maximum acceptable delay of the applications. We show that our model achieves better solutions than a SOTA approach and it is also more flexible. We also present a heuristic solution that is able to achieve near optimal results for real-world networks.

Technological advances in the fifth-generation (5G) mobile networks are based on native cloud computing platforms, and Kubernetes (K8S) has emerged as a relevant orchestration system in this context. However, these platforms were not designed to support 5G services natively. To illustrate, Kubernetes is designed to be agnostic to the services that it orchestrates and cannot dynamically reconfigure the 5G core according to existing network resources, i.e., it provides a partial dynamic orchestration to perform network slicing. This paper proposes a solution integrated with K8S to allow full dynamic orchestration of network slicing at runtime, including online adjustments in the 5G core. This integration is accomplished through a K8S-integrated controller for the control plane. The controller adjusts the 5G core and adapts the virtualized infrastructure. The results show a reconfiguration based on full dynamic orchestration without interruption of the services provided, reducing by close to 50% the full reconfiguration requests number by network slices.

The converging ecosystem provided by Multi-access Edge Computing (MEC) has motivated novel DASH video streaming provisioning scenarios involving the joint coordination of different mechanisms for caching, communication and control. Given the complexity of designing such mechanisms, it is important to provide the research community with open-source tools that support the assessment of their feasibility, specially in real-world environment. Current network emulators still require a significant programming effort to meeting this need. To fill this gap, a new DASH emulator called QoE-DASH is presented in this work. QoE-DASH builds upon goDASH to evaluate the QoE of users consuming DASH content, taking into account network properties, user preferences, and context information. To demonstrate the capabilities of QoE-DASH, we exercise different functionalities of our tool and present a case study where two joint caching and recommendation models, proposed in the literature, are evaluated and their effects on user QoE are depicted using state-of-the-art QoE metrics.

While energy efficiency is an important issue in virtualized RAN due to its impact on OPEX, the centralization level of virtualized RAN functions is another relevant concern that can conflict with the former. In this paper, we introduce a bi-objective problem formulation representing these two objectives and solution strategy based on the ϵ-constraint approach to generate the minimal complete set of Pareto-optimal solutions. We investigate the trade-off between energy efficiency and centralization level in traditional and next-generation RAN topologies. We show scenarios allowing noticeable improvement in the centralization level (e.g., from near 10% to 30%) without impacting energy consumption. However, after a certain value of centralization level, the impact in the energy consumption may become high and hard to justify.

The 5G networks enable new touristic services with challenging communication requirements, such as augmented reality (AR) applications, and allow the visitors to enjoy a touristic experience that involves both the physical and virtual space. Here, we propose a novel multi- user travel itinerary planning framework based on an optimal problem formulation that considers both individual trip itinerary (e.g., tourist's preferences, time or cost) and touristic service constraints (e.g., nearby edge cloud resources and application requirements). The main idea is to maximize the itinerary score of individual visitors, while also optimizing the resource allocation at the edge. We consider two services, video streaming and AR, and evaluate our framework using data from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution adapted to this scenario.

This paper presents a thorough characterization of the user behavior patterns during navigation sessions on Web Mapping Systems (WMSs). Using an extension developed for the Google Chrome, we collected data from nearly 170 users of Google Maps. We present the results of the characterization of over 120,000 URLs. The revealed patterns constitute a valuable tool to plan service optimizations as well as the capacity of WMSs to ensure better user experience.

In this work, we present an approach to performance problem determination in SCS, a CORBA-based component system. Particularly, our approach is built on a monitoring infrastructure provided by the middleware itself and an analysis mechanism based on a Bayesian network model.