The disaggregation of base stations into discrete RAN functions introduces new threats to mobile networks, as failures in one RAN function can trigger cascading failures and disrupt the entire functional chain, impacting network performance and leading to outages. In this paper, we propose the first resilience mechanism leveraging the adaptive placement of RAN functions to mitigate disruptions and recover service continuity in the presence of compromised infrastructure. Our model detects disrupted RUs due to cascading failures, reacts by re-instantiating CU and DU in alternative cloud locations, and recovers service continuity by reestablishing functional chains. We formulate this recovery process as an optimization problem that maximizes post-failure network performance while considering computational and communication constraints of the infrastructure. We numerically evaluated our approach on a real-world mobile network topology under multiple failure scenarios, and demonstrated that our solution recovers up to 70% higher throughput compared to conventional resilience mechanisms.

Virtual Reality Cloud Gaming (VR-CG) is a demanding class of immersive applications that require high bandwidth, ultra-low latency, and efficient resource allocation to deliver a high-quality user experience. In this paper, we propose a scalable, QoE-aware multi-stage optimization framework for VR-CG over 6G networks. Our approach decomposes the joint resource allocation problem into three stages: (i) user association and communication resource allocation; (ii) VR-CG game engine placement with adaptive multipath routing; and (iii) attention-aware scheduling and wireless resource allocation under motion-to-photon latency constraints. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance with significantly reduced computational complexity. We further introduce a user-centric QoE model based on visual attention to virtual objects, enabling adaptive selection of resolution and frame rate. Extensive evaluations using real-world datasets show that, compared to state-of-the-art approaches, the proposed framework improves QoE by up to 50%, reduces communication resource usage by 75%, and achieves up to 35% cost savings, while maintaining an average optimality gap of 5%. Moreover, the proposed heuristics solve large-scale scenarios in under 0.1 s, demonstrating their suitability for real-time deployment in next-generation mobile networks.

The future mobile network schedulers have the complex mission of distributing radio resources among various applications with different requirements. The radio access network (RAN) slicing enables the creation of different logical networks by using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources among the slices to fulfill their requirements. Several recent studies have proposed advances in machine learning-based RRS. However, these works often evaluate their models under limited scenarios and with minimal slice diversity, raising concerns about their real-world applicability. The generalization capabilities of these models remain uncertain without rigorous testing across diverse network conditions and slice configurations, which may hinder their effectiveness upon deployment in operational networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a RAN slicing context. The proposed method protects high-priority slices when the available radio resources are insufficient, using transfer learning to reduce the number of required training steps. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines.

In this article, we propose a novel formulation that jointly considers the Virtualized Network Function (VNF) placement at the Radio Access Network (RAN) nodes and the resource allocation for the Transport Network (TN) of sliced and disaggregated RANs. Unlike most works in the literature that address these network aspects separately, we propose a joint approach. Our proposal ensures an end-to-end delay bound for the Ultra-Reliable and Low-Latency Communications (URLLC) use case in an Industry 4.0 scenario, while simultaneously considering the number of admitted User Equipments (UEs), the transmission rate allocation per slice, the functional split of RAN nodes, and the routing paths in the TN. We use Network Calculus (NC) theory to calculate delay along the TN connecting disaggregated RANs deploying network functions at the Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) nodes. The maximum end-to-end delay is modeled as a constraint in the optimization-based formulation that maximizes the number of admissible flows meeting strict Quality of Service (QoS) requirements, while also taking into account the deployment and operational costs of disaggregated RANs. In this approach, we propose a strategy based on real data from one of the world’s leading Mobile Network Operators (MNOs) to derive coherent estimations to the weights of the proposed objective function. The optimization model leverages a Flexible Functional Split (FFS) approach to provide a new degree of freedom to the resource allocation strategy. Simulation results reveal that, due to its non-linear nature, there is no trivial solution to the proposed optimization problem. Simulation results also show that our proposal guarantees a maximum delay for URLLC use cases in Industry 4.0 while satisfying bandwidth requirements for enhanced Mobile Broadband (eMBB) services.

This demonstration addresses a key open challenge in Open Radio Access Network (O-RAN) deployments: how to intelligently allocate Transport Network (TN) resources to ensure low-latency for mission-critical applications. The demo emulates a Smart Factory scenario where the time-sensitive control traffic of robotic arms competes with industrial camera broadband video streams. We propose an intelligent transport controller that combines network slicing, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Federated Learning (FL) to dynamically prioritize traffic per slice. The architecture uses $\mathbf{P 4}$ switches for local queue monitoring and real-time resource scheduling. The integration with the O-RAN disaggregated stack is based on Open Air Interface (OAI). Experimental results demonstrate valuable load balancing and buffer occupation reduction in the O-RAN midhaul.

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.

Interference mitigation is a common benefit claimed by disaggregated and virtualized radio access networks (vRAN). However, this benefit depends on centralizing the proper virtual network functions (VNFs) from the protocol stack of neighbor radio units (RUs). Additionally, the available computing resources and dynamic demand in RUs must be taken into consideration to obtain efficient results. Naturally, this problem also appears in O-RAN infrastructures which motivates an approach that leverages the O-RAN architecture, including its machine learning-guided design. In this work, we formulate the problem as a Markovian decision process (MDP) and solve it by employing a deep reinforcement learning (DRL) agent. We also describe how our proposal can be implemented inside the O-RAN architecture. Through simulations, we show the improved spectral efficiency provided by the DRL agent while solving the complex VNF placement considering resource constraints, RUs vicinity, and dynamic demand.

The Radio Access Network (RAN) is the segment of cellular networks that provides wireless connectivity to end-users. The O-RAN Alliance has been transforming the RAN industry by proposing open RAN specifications and the programmable Non-Real-Time and Near-Real-Time RAN Intelligent Controllers (Non-RT RIC and Near-RT RIC). Both RICs provide platforms for running applications called rApps and xApps, respectively, to optimize the RAN behavior. We investigate the disaggregation of the Near-RT RIC into components that meet stringent latency requirements while presenting a cost-effective solution. For example, the O-RAN Signalling Storm Protection requires the Near-RT RIC to support end-to-end control loop latencies as low as 10 ms. We propose the novel RIC Orchestrator (RIC-O) that optimizes the deployment of the Near-RT RIC components across the cloud-edge continuum. Edge computing nodes often present limited resources and are expensive compared to cloud computing. Performance-critical components of Near-RT RIC and certain xApps should run at the edge while other components can run on the cloud. Furthermore, RIC-O employs an efficient strategy to react to sudden changes and re-deploy components dynamically. The proposal is evaluated both analytically and through real-world experiments in an extended Kubernetes deployment implementing RIC-O and the disaggregated Near-RT RIC.

This article investigates the deployment of a Near-Real-Time Radio Access Network (RAN) Intelligent Controller (near-RT RIC) on a distributed cloud infrastructure composed of multiple physical sites with different amounts of resources and associated costs. The challenge is dynamically adapting the near-RT RIC deployment to the most cost-effective arrangement while meeting the latency requirements between the near-RT RIC and the controlled nodes. We introduce an optimization model to solve the disaggregated near-RT RIC placement problem, considering a cloud-native infrastructure to minimize the placement cost while satisfying the latency-sensitive control loop requirements across the cloud-edge continuum. Moreover, we describe an experimental environment we created using geographically disparate cloud sites. We present data detailing the latencies of the communication links among these sites and the costs incurred in using this real-world infrastructure. We conduct a performance evaluation of the near-RT RIC deployment, comparing the distributed approach versus a traditional monolithic strategy and evaluating positioning costs, deployment, setup and registration times, and the control loop latency considering three scenarios. Our results show that in a cloud-native environment, the disaggregated near-RT RIC allows cost savings of up to 60% in comparison to a monolithic near-RT RIC while satisfying the control loop latency and achieving time efficiency in terms of deployment and registration of xApps and near-RT RIC components.

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.

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.

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.

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users’ different mobility patterns, which can be challenging, especially for location-based services (LBS). Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users’ privacy and mobility when offloading to the edge. Different privacy and mobility scenarios are simulated and discussed to shed light on the trade-offs (e.g., privacy protection at the cost of increased latency) among privacy protection, mobility, and offloading performance.

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 fifth-generation mobile evolution introduces Next-Generation Radio Access Networks (NG-RAN), splitting the RAN protocol stack into the eight disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. The disaggregated units reach full interoperability on Open RAN. Further advances allow the RAN software to be virtualized (vNG-RAN) on top of general-purpose hardware, enabling the management of disaggregated units and protocols as radio functions. The placement of these functions is challenging since the best decision must be based on multiple constraints, e.g., the RAN protocol stack split, routing paths in network topologies with restricted bandwidth and latency, asymmetric computational resources, etc. The literature does not deal with general placement problems with high functional split options and protocol stack analysis. This article proposes the first exact model for positioning radio functions for vNG-RAN planning, named PlaceRAN, as a Binary Integer Linear Programming (BILP) problem. The objective is to minimize the computing resources and maximize the aggregation of radio functions. The evaluation considered two realistic network topologies, and the results reveal that PlaceRAN achieves an optimized high-performance aggregation level. It is flexible for RAN deployment overcoming the network restrictions, and up to date with the most advanced vNG-RAN design and development.

The fifth-generation mobile evolution enables Next-Generation Radio Access Networks (NG-RAN) transformations. The RAN protocol stack is split into eight disaggregated options combined in three network units, i.e., Central, Distributed, and Radio. Further advances allow the RAN functions to be virtualized on top of general-purpose hardware using the virtualized RAN (vRAN). The combination of NG-RAN and vRAN results in vNG-RAN, enabling the management of the disaggregated units and protocols as a set of radio functions. However, the orchestration-based placement of these radio functions is challenging since the best decision can be determined by multiple constraints involving RAN disaggregation, crosshaul network requirements, availability of computational resources, etc. This article proposes OPlaceRAN, a vNG-RAN deployment orchestrator framed within the NFV reference architecture and aligned with the Open RAN initiative. OPlaceRAN supports the dynamic placement of radio functions focusing on vNG-RAN planning and is designed to be agnostic to the placement optimization solution. We developed a prototype based on cloud-native tools to deploy RAN using containerized virtualization and the OpenAirInterface emulator. The evaluation is analyzed considering two different approaches as a proof-of-concept. First, we applied two placement solutions in a controlled real computing infrastructure with a crosshaul network. Second, we investigated the orchestrator’s scalability with a real and larger-scale topology. Our results show that OPlaceRAN is an effective cloud-native solution for containerized network function placement and agnostic to the placement solution, handling scale-out well. OPlaceRAN is up-to-date with the most advanced vNG-RAN design and development approaches, contributing to the evolution of fifth-generation networks.

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.

The process of identifying and authenticating Internet of Things (IoT) devices based on the electromagnetic characteristics of their wireless interfaces is a topic that has been receiving a lot of attention lately due to the continuous growth in the market of embedded and wearable wireless devices. Securing the networks to whom these devices are connecting daily has become a significant security challenge, which is threatening the security and safety of thousands, maybe millions of private and public networks due to the vulnerable nature of wireless devices to a well-known set of possible attacks.In this paper, we present the initial results acquired from our work-in-progress to develop new radio-features-extraction-based technique to identify wireless devices, specifically Internet of Things (IoT) ZigBee and LoRa devices. This paper summarizes our initial experimental setup to gather and analyze the devices signals to extract the desired features and describes the signal pre-processing approach and the machine learning methods that we attempted to date.

The virtualized radio access network (vRAN) placement problem consists of jointly choosing a functional split and the placement of virtualized network functions on vRAN nodes scattered in the network. The most prominent solutions present optimal approaches to solve the problem, but they are computationally expensive for large instances. Non-exact approaches emerge as alternatives to solve the vRAN placement problem, mainly using machine learning, which is largely fostered by the standardization bodies in next-generation networks. In this context, we present an approach to solve the problem using deep reinforcement learning (DRL), where the objective is to jointly minimize the number of computing resources used and maximize the vRAN centralization level. To build our DRL agent, we started from a traditional optimization formulation that guided the agent development inside a conventional DRL framework. We compare our solution with two exact optimization models from the literature, including one that has a DRL solution. Since our proposed design was based on a most advanced optimal model, it was able to outperform one of the exact optimization models and, as a consequence, its DRL agent.

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.

Disaggregated and virtualized RANs (vRANs) offer the opportunity for flexible and efficient use of computing resources through the proper placement of the RAN Virtualized Network Functions (VNFs). However, many works neglect the necessary coordination between VNF placement and the pro-cessing of the RAN tasks inside these VNFs. This can negatively impact important tasks such as resource scheduling and interference control. In this work, we introduce a new approach for VNF placement that is aware of the wireless coverage and its associated tasks. Our solution was designed in the context of O-RAN architecture, exploring functionalities of monitoring and closed-loop decision making. Simulation results illustrate the benefits of our solution, mainly related to improvements for edge users who are exposed to the worse conditions of spectral efficiency and throughput.

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.

Video data transmission has increased notably in the last years and the projections indicate they will pose a huge demand over the mobile wireless networks. This has been one of the motivations to improve the infrastructure with new technologies, but also to use efficiently the available resources, avoiding unnecessary increasing in CAPEX/OPEX. Distributed caching is a promising approach, since it employs the existing network resources in an opportunistic way to offload the bottlenecks in the content distribution of the mobile networks. The viral videos increase the probability of this content being shared between the Online Social Networks (OSNs). While solutions have already been proposed to exploit this information, the current state-of-the-art still fails to take into account online social interactions for distributed video caching. This work presents a novel approach, called Probabilistic Social Cascade for D2D communication (ProSoCaD), for distributed caching that employs knowledge from OSNs. We have carried out an in-depth simulation study in ns-3 to assess the performance of ProSoCaD. ProSoCaD improves the offload rate, lowers energy consumption, and reduces the delay in obtaining access to the video content.

Resource sharing in mobile wireless networks has been employed to reduce costs, extend coverage, and ease the entry of new players in the market. The introduction of programmability and virtualization is expected to amplify these benefits of resource sharing. In this paper, we study a new virtualization-based paradigm for resource sharing in mobile wireless networks. Specifically, we consider the problem of resource allocation, particularly when user demands are uncertain. We formulate several two-stage sequential stochastic allocation schemes that provide tradeoffs between cost and user satisfaction. These allocation schemes are studied under different resource provider pricing models. Our simulations demonstrate that: First, while reducing cost significantly, virtualization considerably improves user satisfaction, and virtualization gains increase with the number of operators that share resources. Second, the improvements in cost, user satisfaction, and resource usage increase substantially with the level of user clustering.

Two promising solutions have been recently proposed to address the massive growth in mobile traffic and wireless devices: LTE-U and in-band full-duplex (FD) wireless. LTE-U extends the benefits of LTE-A to the unlicensed 5 GHz band, used mainly by Wi-Fi users. However, the uncertainty in Wi-Fi user activities makes provisioning QoS guarantees to LTE-U users challenging. On the other hand, FD wireless can double spectrum efficiency by enabling simultaneous transmission and reception over the same frequency band. Our objective in this paper is to exploit excess capacity of deployed Wi-Fi networks (operating in the 5 GHz band) to orchestrate a ‘robust’ virtual LTE-U network from a hybrid set of half-duplex (HD) and FD Wi-Fi access points (APs). Although the orchestrated LTE-U network does not support deterministic QoS guarantees, it is designed to provide prespecified probabilistic QoS guarantees (hence, it is robust). Towards achieving our goal, we develop novel stochastic resource allocation formulations that optimally orchestrate a virtual LTE-U network from a hybrid set of HD/FD APs with the minimum cost. We first consider the single small-cell problem and propose a stochastic formulation, which we refer to as CCLTEUsingle. Then, we study the multi-cell stochastic allocation problem and develop another formulation, which we refer to as CCLTEUmulti. Our formulations adopt a ‘chance-constrained stochastic programming’ approach. We derive the deterministic equivalent programs of CCLTEUsingleand CCLTEUmultiand evaluate them numerically under various system parameters.

Wireless Mesh Networks (WMN) provide ubiquitous Internet access for mobile users by integrating wired and wireless networks. As gateways towards wired networks are potential bottlenecks, load balancing routing plays a central role in the performance of WMN. With regard to this matter, a number of load balancing routing heuristics have been proposed. However, none of them tackles four key aspects of the load balancing routing problem at the same time, which are achieving low computational costs, and reducing the average path length and routing overhead, while distributing the flows uniformly. To fill this gap, the aim of this paper is to introduce a new load balancing routing heuristic, called BPR - Bottleneck, Path length and Routing overhead, which offers an efficient online solution by taking into consideration all these aspects of the load balancing routing problem. We have carried out a simulation study to compare the performance of BPR with a recent related work. BPR obtains bottleneck values within desirable bounds, while reducing the average path length. As a result, BPR also notably reduces the number of route updates in the network, i.e. the routing overhead. Finally, we show that BPR is simple and has low demand for processing requirements.

The massive deployment of NAT/firewall devices in the Internet has greatly affected its end-to-end connectivity. Several applications, in particular Grid computing systems which span several Autonomous Domains require the communication among hosts behind NAT/firewall. Despite the existence of successful techniques for the establishment of UDP flows between hosts behind NAT/firewall, the same does not hold for TCP. Furthermore, existing techniques must be implemented individually by each application, possibly causing code duplication, or depend on relay servers, making it prone to performance problems. This work proposes a strategy that allows application processes behind NAT/firewall to communicate transparently, on top of any transport protocol. The system works by establishing IPv6-over-UDP tunnels between hosts, in which IPv6 packets are encapsulated within UDP data grams and are sent through a UDP hole punching session. A detailed description of the proposed system, case studies and experimental results are presented.

This paper investigates the joint use of push-out mechanisms with random early detection (RED)-like discarding policies to support service differentiation in the Internet. The efficiency and the degree of differentiation of a complete sharing with push-out, a RIO (RED with in/out) and a RIO with push-out queues are assessed. Results indicate that push-out used jointly with RED-like policies does not improve the performance.