AI-driven Network Function Virtualization (NFV) service chaining is emerging as a key enabler for automation in edge-to-cloud infrastructures. However, existing standards and orchestration frameworks lack mechanisms for fairness, coordination, and dynamic resource sharing across distributed AI agents. This paper proposes a hierarchical architecture that integrates adaptive agent placement and fairness-aware scheduling for AI-based NFV coordination. At its core, the Multi-Agent Dynamic Bandwidth Environment (MADBE) framework leverages deep reinforcement learning to enable agents to collaboratively allocate shared bandwidth while meeting latency and throughput constraints. Experimental results demonstrate that MADBE significantly improves convergence speed, reduces conflict rates, and maintains bandwidth utilization near the 90% threshold, outperforming state-of-the-art baselines. Moreover, MADBE sustains near-zero violation rates for service-level constraints, even under dynamic and heterogeneous workloads. These results highlight the potential of fairness-aware, multi-agent coordination in next-generation 5G/6G networks and industrial automation environments.

In industrial multi-access edge computing (MEC), novel collaborative systems involving multiple automated guided vehicles (AGVs) require the execution of both critical tasks and computational tasks, such as AI-based collision avoidance. However, previous research focused mainly on scheduling process-related tasks and overlooked computational tasks. Moreover, scheduling tasks between AGVs in collaborative systems poses an integer problem, which is challenging to solve in polynomial time, highlighting the need for computationally efficient algorithms. This paper proposes a deep reinforcement learning (DRL)-based approach to multi-task scheduling (DRL-MTS) in multi-AGV systems. It involves dynamically applying a catalog of DRL models, each tailored to different numbers of AGVs. Evaluation results demonstrate that the proposed inter-AGV DRL-MTS strategy closely approaches the optimal solution, reaching up to 96% task completion compared to a 98% of the optimal solution, while significantly reducing decision times. Moreover, the training time for these models has been reduced threefold using datasets from existing optimization solvers, and transfer learning has further cut training times by up to 51%.

Resource distribution among diverse administrative domains, network operators, and geographical locations across the edge-to-cloud continuum requires suitable communication and management and orchestration (MANO) mechanisms among orchestration domains. In multi-access edge computing (MEC) environments, efficient application lifecycle management and system federation are essential for scalability, optimal resource utilization, and ensuring service continuity and reliability. Existing orchestration solutions, typically designed for centralized cloud architectures, often fall short in accommodating application delay requirements and in managing the dynamic and distributed nature of MEC resources effectively. This paper introduces a cloud-native, platform-agnostic MEC Orchestrator (MEO) with enhancements over the ETSI MEC architecture, albeit aligned with GSMA and ETSI MEC federation standards, that supports cross-platform MANO and resource controllability. Federation is supported through a new MEO-to-MEO interface that enables application migration across MEC systems. Experimental results demonstrate a 95% instantiation success rate for instantiation, overperforming the baseline Kubernetes scheduler, and 93.3% for migration requests within federated MEC systems in high request volume scenarios.

The advancement towards B5G/6G relies on the synthesis of connect-compute platforms and their use in highly heterogeneous clusters featuring hardware accelerators. While these accelerators offer improved computational efficiency, sill, they make development, deployment, and orchestration of services more complex, with limited flexibility, and necessitate domain-specific knowledge. In AI@EDGE we are targeting seamless integration of such diverse platforms for executing AI-related tasks. This paper focuses on acceleration aspects and presents a MEC system that facilitates AI servicing over a cluster of FPGA, GPU, and CPU nodes. To this end, we develop our custom tools for generating multi-variant AI models, informative function descriptors, flexible MEC orchestrators, and runtime resource managers. The results show successful interoperability, with generic Python models getting deployed/migrated across distinct platforms for performance gains in the area of 10x.

5G networks promise to deliver an unprecedented performance that can accommodate novel services with stringent Quality of Service (QoS) requirements that were not possible with previous generations of networks. Edge Computing plays a fundamental role by providing computing resources closer to the user, reducing round trip times. However, the deployment of edge computing poses new challenges, including the energy footprint of a potentially large number of servers. Even in idle state, these servers consume a significant amount of energy, which is worth considering for reducing their energy footprint. In cloud computing environments, server shutdown during low-demand periods is a typical energy-saving strategy. However, this approach has received less attention in edge computing due to the strict latency requirements of its use cases. This work presents ODESA, an edge server shutdown strategy with polynomial time complexity that provides a tradeoff between the idle energy consumption of the edge servers and energy consumed by the backhaul to route requests to active servers. Our numerical investigation shows that thanks to the reduction in idle energy consumption, ODESA reduces the total consumption by 42% over the common always-on approach during low-demand periods and 11% over 24 hours, all while meeting the latency requirements of the applications.

While advances in wireless networks enable novel services with previously unreachable latency guarantees, edge computing becomes essential for delivering computing resources close to the users and meeting the strict latency requirements. However, addressing the energy footprint of computing resources is crucial amid the pressing sustainability concerns. The energy consumption of idle resources accounts for a significant part of the total energy footprint. While server shutdown during low-demand periods is common in cloud computing, it is challenging to determine which edge servers to shut down and how to route requests due to the stringent latency requirements of the applications. Thus, this work formulates an optimal orchestration policy to minimize the energy consumption of the edge computing infrastructure and presents LESS-ON, a strategy with a polynomial time complexity that reduces the operational energy footprint of edge computing by shutting down edge servers during low-demand periods. In contrast to previous studies, LESS-ON considers the energy requirements associated with routing requests to the designated edge servers. Our numerical evaluation shows that LESS-ON reduces the total consumption by 42% with respect to the common always-on approach during low-demand periods and by 35% over 24 h, all while meeting latency requirements.

Abstract While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network’s edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions.

Virtualization in cellular networks is one of the key areas of research where technologies, infrastructure and challenges are rapidly changing as 5G system architecture demands a paradigm shift. This paper aims to study the viability and the performance of cloud-native infrastructures for hosting network functions. The selected frameworks implement both the 4G and the 5G stacks and their network functions. This work considers a variety of scenarios for enabling the deployment of a distributed and open-source cellular network: a baremetal setup, an all-docker-based setup and the proposed Kubernetes setup. Moreover, an analysis of the impact that the Radio Access Network (RAN) and the Core Network (CN) have on computational resource utilization is presented as the network conditions vary. The design proposed in this work has been validated and analyzed using the proposed prototype and testbed. This paper proposes a design to increase resource usage flexibility and performance and reduction of deployment time. The analysis of the gathered data reveals that the deployments of containerized cellular networks display better performance in terms of flexibility, low startup times, and ease of deployment while consuming the same resources as the non-containerized.

Edge computing has transformed cellular networks, offering fast response times by moving computing resources to the network's edge. This not only reduces the burden on the Wide Area Network (WAN) but also enables latency-sensitive applications. However, the widespread deployment of edge computing raises concerns regarding its sustainability. In this work, we present MintEDGE, a simulation framework that models a fully configurable edge-enabled cellular network. MintEDGE empowers researchers and practitioners to design and assess energy-saving strategies for edge computing. We discuss the details of the simulator and its customizable elements like user mobility, the possibility to use predictive workload algorithms, and diverse application scenarios at scale. MintEDGE is released under a permissive MIT license.

Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. This paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. This study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologies that can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.

With the forthcoming of 5G networks, the underlying infrastructure needs to support a higher number of heterogeneous services with different QoS needs than ever. For that reason, 5G inherently provides a way to allocate these services over the same infrastructure through the concept of Network Slicing. However, to maximize revenue and reduce operational costs, a method to proactively adapt the resources assigned to each slice becomes imperative. For that reason, this work presents two Machine Learning (ML) models, leveraging Long-Short Term Memory (LSTM) and Random Forest algorithms, to forecast the throughput of each slice and adapt accordingly the amount of resources needed. The models are evaluated using NS-3, which has been integrated with the ML models through a shared memory framework. This enables a closed loop in which the predictions of the models can be used at run time to introduce changes in the network. Consequently, it makes it able to cope with the forecasted requirements, eliminating the need for off-line training and resembling better a real-life scenario. The evaluation performed shows the ability of the models to predict the slices' throughput under various settings and proves that Random Forest provides up to 26% better results than LSTM.

O-RAN is currently emerging as the way to build a virtualized 5G and beyond Radio Access Network (RAN) that is based on open interfaces and off-the-shelf hardware. O-RAN consolidates the intelligence of several gNodeBs at the Near-realtime RAN Intelligent Controller (RIC) making it more programmable and aware of the mobile users’ surroundings. In this paper we present Roadrunner, an O-RAN-based solution designed to improve cell selection in 5G and beyond networks. Our work has been motivated by the fact that the legacy cell selection procedure in both 4G and 5G networks tends to prefer radio quality and seamless connectivity to high data rates. The reason for this can be traced back to the older releases of the mobile network architecture that were optimized for the circuit-switched communication paradigm and for sparse network deployments. However, with an O-RAN-based approach we can leverage the global network view built and maintained by the Near-realtime RIC to jointly optimize mobility management for channel quality and bitrate. We have designed Roadrunner following the O-RAN Alliance design principles and without requiring any change to the existing 3GPP signaling. No changes to the mobile devices are required either. Performance measurements carried out on a small scale testbed show how Roadrunner can almost double the median throughput in some specific traffic scenarios while also achieving better network fairness.

Handling High-Performance Computing (HPC) workflows often requires the orchestration of a collection of parallel flows. Traditional techniques to optimize flow-level metrics do not perform well in optimizing such collections because the network is usually agnostic to application requirements. A Coflow is a recently proposed abstraction that created new opportunities in network scheduling for datacenter networks. However, recent work on coflow scheduling has focused on merely two objectives: decreasing communication time of data-intensive jobs and guaranteeing predictable communication time. In this paper, we take a step further and propose some initial results towards the design of heuristics that optimize also the energy consumption of a data center that hosts HPC jobs. To this aim, we built and released an energy-aware coflow scheduling simulator to the community that helps analyze the tradeoff between energy efficiency and coflow completion time. We also propose two scheduling algorithms that consider coflow completion time, CPU utilization, and energy consumption efficiency. Our initial results using the simulator clarify how each policy should be tuned to the application needs and the computational resources available.

The COVID-19 emergency has made the consumption of multimedia content skyrocket in all contexts, including education. Many universities leverage hybrid learning models, in which students join a real-time video session via Wi-Fi from several classrooms to ensure safety and social distancing. This is creating a significant strain on the wireless access network, which is required to deliver an unusually high level of traffic. Artificial Intelligence (AI) and Machine Learning (ML) solutions have emerged as a way to make networks easier to control and to manage. However, their black box nature and in general their fire and forget approach has generated considerable skepticism over the entire value chain, from vendors to network administrators. This situation has led to a new interest in interpretable AI solutions, which aim at making the decisions taken by AI/ML models intelligible to a domain expert. In this article, we review the concept of interpretable AI and analyze the challenges, requirements, and benefits it can bring to delay-sensitive content delivery in 802.11 Wi-Fi networks. Furthermore, we apply these requirements to a use case in which we focus on advanced Quality of Service (QoS) provision, and we propose an interpretable and low-complexity ML model that addresses those requirements. The results demonstrate performance gains up to 60% in the sensitive traffic and up to 20% at network-wide level.

Cloud Native (CN) in 5G systems has been identified as a pivotal candidate for operational and capital expenditure savings as well as for improvements in system agility and services role-out. CN telco is a step forward with respect to Network Function Virtualisation (NFV) aiming at embracing a microservice-based architecture. With this in mind, the European Telecommunications Standards Institute (ETSI) has evolved the ETSI NFV reference architecture to adapt to CN and fill the gap with the NFV framework, including containers and ZeroTouch, among other capabilities. Open-source Management & Orchestration (MANO) initiatives, such as Open Source MANO (OSM), are promoting this adoption giving support to CN solutions based on containers. However, at this early stage deployments are currently non-standalone and embedded in VNF-based solutions such as OpenStack. In this context, this paper presents a proof of concept of a full container technology deployment -via Kubernetes- in a NFV architecture. First, a full CN NFV environment is set with the help of OSM MANO, for which we describe the implementation to enable native kubernetes-based Container Network Functions (CNFs) and analyse their performance, limits, advantages and drawbacks. Finally, our solution for CNFs is benchmarked against a typical OSMOpenStack setup where VNFs are deployed. The results obtained in this work can help to further encourage users and operators to use CNFs and get the most out of containerisation in NFV.

Abstract Wireless technologies are continuously evolving, including features such as the extension to mid- and long-range communications and the support of an increasing number of devices. However, longer ranges increase the probability of suffering from hidden terminal issues. In the particular case of Wireless Local Area Networks (WLANs), the use of Quality of Service (QoS) mechanisms introduced in IEEE 802.11e compromises scalability, exacerbates the hidden node problem, and creates congestion as the number of users and the variety of services in the network grow. In this context, this paper presents a configurable Colored Petri Net (CPN) model for the IEEE 802.11e protocol with the aim of analyzing the QoS support in mid- and long-range WLANs The CPN model covers the behavior of the protocol in the presence of hidden nodes to examine the performance of the RTS/CTS exchange in scenarios where the QoS differentiation may involve massive collision chains and high delays. Our CPN model sets the basis for further exploring the performance of the various mechanisms defined by the IEEE 802.11 standard. We then use this CPN model to provide a comprehensive study of the effectiveness of this protocol by using the simulation and monitoring capabilities of CPN Tools.

The ongoing roll-out of 5G networks paves the way for many fascinating applications such as virtual reality (VR), augmented reality (AR), and autonomous driving. Moreover, 5G enables billions of devices to transfer an unprecedented amount of data at the same time. This transformation calls for novel technologies like multi-access edge computing (MEC) to satisfy the stringent latency and bitrate requirements of the mentioned applications. The main challenge pertaining to MEC is that the edge MEC nodes are usually characterized by scarce computational resources compared to the core or cloud, arising the challenge of efficiently utilizing the edge resources while ensuring that the service requirements are satisfied. When considered with the users' mobility, this poses another challenge, which lies in minimization of the service interruption for the users whose service requests are represented as service function chains (SFCs) composed of virtualized network functions (VNFs) instantiated on the MEC nodes or on the cloud. In this paper, we study the problem of joint user association, SFC placement, and resource allocation, employing mixed-integer linear programming (MILP) techniques. The objective functions of this MILP-based problem formulation are to minimize (i) the service provisioning cost, (ii) the transport network utilization, and (iii) the service interruption. Moreover, a heuristic algorithm is proposed to tackle the scalability issue of the MILP-based algorithms. Finally, comprehensive experiments are performed to draw a comparison between these approaches.

Connected and automated vehicles currently rely on on-board resources to implement autonomous functions, leaving the mobile network for non-mission-critical applications. At the same time, the ultra-low latency, the increased bandwidth, and the softwarization and virtualization technologies of 5G systems are opening the door to multiple applications in the context of connected and automated vehicles. The deployment of applications at the edge of the mobile network under the Multi-access Edge Computing (MEC) paradigm becomes an excellent option for meeting the latency requirements imposed by connected mobility. In this context, this demonstration showcases how remote and autonomous driving applications, such as lane tracking and object detection, can be offloaded to a MEC-enabled 5G network without impairing their effectiveness, and the change in the latency perceived by end-users with respect to a cloud deployment.

Software-Defined Networking promises to deliver a more manageable network whose behaviour could be easily changed using applications written in high-level declarative languages running on top of a logically centralized control plane resulting, on the one hand, in the mushrooming of complex point solutions to very specific problems and, on the other hand, in the creation of a multitude of network configuration options. This fact is especially true for 802.11-based Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach. In this paper we present aiOS, an AI-based Operating System for SD-WLANs. Then, we use aiOS to implement several Machine Learning (ML) models for user-adaptive frame length selection in SD-WLANs. An extensive performance evaluation carried out on a real-world testbed shows that this approach improves the aggregated network throughput by up to 55%. Finally, we release the entire implementation including the controller, the ML models, and the programmable data-path under a permissive license for academic use.

Wireless technologies have come to stay, fuelled by the digital world in a global culture that expects instant access to information. The explosive increase in Wi-Fi enabled devices and the incessant demand for high quality multimedia contents require to find innovative ways for accommodating these latency sensitive applications. In this Dissertation we explore multimedia content distribution over IEEE 802.11 networks and tackle the existing difficulties through three main approaches. First, we endeavour to enhance the channel access and the QoS provisioning by relying on machine learning models. Then, we leverage the SDN paradigm to provide efficient radio resource management through adaptive channel assignment and traffic distribution methods. Finally, we propose an integral SDN-based solution to address the shortcomings found in multicast multimedia transmissions in Enterprise WLANs.

The deployment of applications and services at the edge of the mobile network under the Multi-access Edge Computing (MEC) paradigm is an excellent option when strict latency requirements must be satisfied. In this work we introduce LightEdge, a lightweight, ETSI-compliant transparent MEC solution for 4G and 5G networks. LightEdge requires zero modifications to the operator’s environment During the demo we will demonstrate how a typical web application can be offloaded to a MEC-enabled 5G network without impairing the effectiveness of the application.

Although 4G and 5G Radio Access Technologies(RATs) aim to usher in faster connectivity that is able to cope with mobile traffic demands, this capability is sometimes hindered by poor indoor signal quality caused by distance from base stations and the materials used in the construction of buildings. These factors have led to Wi-Fi being adopted as the technology of choice in indoor scenarios. Although the deployment of Wi-Fi Access Points (APs) can be planned, the user-AP association procedure is not defined by the standard but left to the vendor's choice, which for simplicity is usually driven by signal strength. This approach leads to uneven user distributions and poor resource utilization. To overcome this rigidity, in this paper, we leverage SoftwareDefined Networking (SDN) to propose ajoint user association and channel assignment solution in Wi-Fi networks. Our approach considers average signal strength, channel occupancy, and AP load to make better user association decisions. Experimental results have demonstrated that the proposed solution improves the aggregated goodput by 22% with respect to approaches based on signal strength. Furthermore, user level fairness is also improved.

Despite the planned operation of enterprise wireless local area networks (WLANs), they still experience unsatisfactory performance due to several inefficiencies. One of the major issues is the so-called sticky user problem, in which users remain connected to an access point (AP) until the signal quality becomes too weak. In this paper, we leverage software-defined networking (SDN) to propose a user association solution for WLANs aiming to mitigate such inefficiencies, thus improving resource utilization. As it is a computationally hard problem, we also design various low-complexity user-AP association schemes that consider not only signal quality but also AP loads and minimum quality requirements for user traffic. Moreover, to provide simultaneous content distribution in a sustainable mode, we propose exploiting link-layer multicasting to decide on user-AP associations. Our analysis via simulations and experimentation on an open-source testbed shows that considering user-AP association jointly with multicast delivery leads to a significant performance increase over the default client-driven approach: the median throughput is 11× higher when all users request the same content and the achieved improvement decreases to 68% for 100 contents. Moreover, due to more efficient use of the airtime, unicast users achieve higher throughput if multicast delivery is exploited.

Connected and automated vehicles currently leverage on-board resources to implement autonomous and assisted driving operations. Such functionalities, which are characterized by tight latency demands, require significant processing resources and can generate a considerable amount of data. Cloud computing is considered the one-stop solution for executing computationally intensive workloads. However, accommodating autonomous and assisted driving requirements using a centralized cloud computing platform is not always feasible due to the latency and reliability constraints they impose. In this paper, we introduce a multi-access edge computing platform suitable for offloading certain autonomous and assisted driving tasks to the edges of the network. We also illustrate how both paradigms (centralized and edge cloud computing) can coexist complementing each other in the challenging task of supporting autonomous and assisted driving, thus opening up new horizons for connected vehicles, for which service instantiation and migration needs to be seamless due to its impact on road safety.

Nowadays mobile networks are asked to support different applications and services characterized by very specific Quality of Service (QoS) requirements. With this aim in mind, deploying network slices with particular resource allocation policies on a per-service basis becomes extremely relevant. In this regard, we introduce a solution able to dynamically partition the underlying physical infrastructure of a mobile radio access network into multiple logical slices with distinctive service-level agreements. We leverage Software-Defined Networking principles to provide fine-grained flow identification and sophisticated QoS management policies on a generic architecture supporting 4G and 5G networks with the objective of mapping the path towards the future mobile networks. The experimental evaluation of the deployed prototype on a real-world testbed has demonstrated the slicing capabilities of the system while ensuring full performance and functional isolation. We release the entire implementation under a permissive APACHE 2.0 license for academic use.

Multi-access Edge Computing (MEC) is regarded as a pivotal pillar to grasp the particularized 5G goals by shifting network intelligence from the cloud to the edge. Network Function Virtualization (NFV) emerged as a paradigm intending to replace traditional vendor-specific network appliances with software instances of the network functions capable of running on standard devices. Recently, deploying softwarized network functions at the network edge has gained an unprecedented attention. Multiple virtualization technologies can be utilized to deploy virtualized network functions including Virtual Machines (VMs), containers, and unikernels. However, each virtualization platform has specific advantages and disadvantages, which makes worthy studying their real performance. This is specially important when it comes to implement network functions at the edge of 5G networks, where resources are scarce and quick response to user requests is needed. In this regard, this paper studies the performance of virtualization technologies by deploying two services namely Apache and Redis and provides an extensive experimental campaign and conclusive results.

Software-defined networking (SDN) is making their way into the fifth generation of mobile communications. For example, 3GPP is embracing the concept of control-user plane separation (a cornerstone concept in SDN) in the 5G core and the radio access network (RAN). In this paper, we introduce a flexible, programmable, and open-source SDN platform for heterogeneous 5G RANs. The platform builds on an open protocol that abstracts the technology-dependent aspects of the radio access elements, allowing network programmers to deploy complex management tasks as policies on top of a programmable logically centralized controller. We implement the proposed solution as an extension to the 5G-EmPOWER platform and release the software stack (including the southbound protocol) under a permissive APACHE 2.0 license. Finally, the effectiveness of the platform is assessed through three reference use cases: 1) active network slicing; 2) mobility management; and 3) load-balancing.

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

The increasing demand for live streaming and for remote sensing applications is bringing renewed interest on uplink performances in Wi-Fi networks. Radio diversity can improve the performance of such applications by opportunisti-cally receiving mobile users' traffic at multiple attachment points. However, radio diversity techniques can not be used in standard Wi-Fi networks due to backwards compatibility problems. In this paper we present Wi-Not, a novel SDN-based solution for exploiting radio diversity in software-defined WLANs. Wi-Not allows mobile terminals to be associated to multiple Wi-Fi APs in the uplink direction improving frame delivery probability in uplink-constrained applications. Wi-Not does not require changes to the mobile terminals and can be easily deployed with minimal changes to the network infrastructure. An experimental evaluation carried out over a real-world testbed shows that this approach can deliver an improvement of up to 80% in terms of UDP goodput and up to 60% of TCP throughput. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

In traditional 802.11 networks stations usually try to associate to the AP with the highest signal strength. However, especially in case of very dense deployments, this may lead to uneven wireless clients distribution, and thus to poor network performances. Software Defined Networking (SDN) has recently emerged as a novel approach for network control and management. In this paper we present Wi-Balance, a novel SDN-based solution for joint user association and channel assignment in Wi-Fi networks. An experimental evaluation in a real-world testbed showed that Wi-Balance outperforms the RSSI-based user association schemes in terms of throughput and channel utilization by up to 25% and 30%, respectively. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

Current 802.11-based WLANs are asked to support an ever increasing number of services and applications, each of them characterized by a diverse set of requirements in terms of bitrate, latency, and reliability. Network virtualization and programmability are two emerging trends that can support the realization of such a vision in a cost-effective fashion. In this paper we introduce Lasagna, a novel end-to-end solution that enables flexible management of slices encompassing both the wired and the wireless segments of an Enterprise WLAN. Lasagna allows flexible management of network slices to meet their respective service requirements. An experimental evaluation carried out over a real-world testbed shows that Lasagna can ensure both functional and performance isolation between the different slices and efficient radio resource utilization. We release the entire implementation including the controller and the datapath under a permissive license for academic use.

The ever-increasing demand for mobile content delivery and multimedia services is bringing renewed interest in multicast communications in Wi-Fi based WLANs. Nevertheless, multicast over Wi-Fi raises several challenges including low data rates and coexistence issues with other unicast streams. Some amendments to the Wi-Fi standard, such as 802.11aa, have introduced new delivery schemes for multicast traffic as well as finer control on the low-level aspects of the 802.11 medium access scheme. However, the logic for using such features is left to the implementer of the standard. In this paper, we present SDN@Play Mobile, a novel SDN-based solution for joint mobility management and multicast rate-adaptation in Wi-Fi networks. The solution builds upon a new abstraction, named Transmission Policy, which allows the SDN controller to reconfigure a multicast transmission policy when its optimal operating conditions are not met. An experimental evaluation carried out over a real-world testbed shows that our approach can deliver significant improvements in terms of both throughput and channel utilization compared to the legacy 802.11 multicast scheme. Finally, we release the entire software implementation under a permissive APACHE 2.0 license for academic use.

Wireless network research and development efforts are largely driven by the increasing interest in multimedia applications. Video streaming services, which often involve strict quality of service requirements and are very sensitive to delays, represent a significant proportion of these applications. In IEEE 802.11-based WLANs, these services raise several challenges in terms of robustness, reliability, and scalability, especially when supporting multiple multicast streams at the same time. Nevertheless, traditional network architectures make it difficult to address these problems. In this context, the software defined networking (SDN) paradigm opens new research possibilities by decoupling the control decisions from the data-plane and by improving network management and programmability. In this paper, we present SM-SDN@Play, an SDN-based solution for joint multicast rate selection and group formation in 802.11-based networks. Experimental results show the high performance and reliability capabilities of the scheme, regardless of the application bitrate, the number of clients, and the number of concurrent multicast streams. Furthermore, the channel utilization is greatly reduced with regard to the standard multicast schemes, which allows other applications to be supported without experiencing a performance degradation. We release the entire software implementation under a permissive APACHE 2.0 license for academic use.

In view of the exponential growth in the live multimedia content applications, multicast communications would imply a considerable traffic reduction compared to the unicast ones. However, given the absence of feedback information, packets are sent at the lowest rate, hence occupying the medium for long periods. Software Defined Networking (SDN) has changed the traditional network operations, therefore simplifying the network management and resource allocation. On this basis, in this demo we present an SDN-based algorithm for the dynamic multicast rate adaptation in Wi-Fi networks that outperforms the channel usage of the IEEE 802.11 standard, while maintaining the quality of the transmission. The performance of our solution has been preliminarily tested on a real-world testbed, therefore proving how it can be run on any WLAN infrastructure.

Live multimedia applications have experienced a dramatic growth due to the development of High Definition (HD) contents. Most of these streams are targeted at groups of people, who usually use Wi-Fi networks to access the platforms. In this context, a renewed interest in multicast applications has arisen, which usually suffer low data rates and reliability issues. Software Defined Networking (SDN) has revolutionized the traditional network architecture management, hence allowing for new ways to address the most challenging network constraints. In this paper, SDN@Play is presented as an SDN-based algorithm to dynamically adapt the multicast data rate. The implementation over a real-world testbed has proved that this solution is able to outperform the channel occupancy rate of the IEEE 802.11 standard, while keeping the the reliability of the transmission. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

The increasing demand for multimedia content and for live broadcasting is bringing renewed interest in multicast applications. In many cases, users access such streams using Wi-Fi networks. However, multicast over Wi-Fi poses several challenges including low-data rates and coexistence issues with regard to other unicast streams. Software Defined Networking (SDN) has recently emerged as a novel approach to network control and management. In this paper we present SDN@Play, a novel SDN-based solution for multicast rate-adaptation in Wi-Fi networks. The solution builds upon a new abstraction, named Transmission Policy which allows the SDN controller to reconfigure or replace a certain rate control policy if its optimal operating conditions are not met. An experimental evaluation carried out over a real-world testbed shows that this approach can deliver an improvement of up to 80% in terms of channel utilization compared to legacy 802.11 multicast. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

The high demand for wireless connectivity and the increase in the applications bitrate have lead to deploy denser and heterogeneous networks. However, an inefficient management of the network resources may arise poor performance and collision issues, therefore presenting a new set of challenges. In this demo, we will leverage on the Software Defined Networking (SDN) paradigm to show Wi-Balance, an algorithm able to achieve an effective balance of the traffic load in Wi-Fi networks with the aim of providing an optimum distribution of the network resources and improving the global performance.

Multimedia communications have experienced an unprecedented growth due mainly to the increase in the content quality and the emergence of smart devices. The demand for these contents is tending towards wireless technologies. However, these transmissions are quite sensitive to network delays. Therefore, ensuring an optimum QoS level becomes of great importance. The IEEE 802.11e amendment was released to address the lack of QoS capabilities in the original IEEE 802.11 standard. Accordingly, the Enhanced Distributed Channel Access (EDCA) function was introduced, allowing it to differentiate traffic streams through a group of Medium Access Control (MAC) parameters. Although EDCA recommends a default configuration for these parameters, it has been proved that it is not optimum in many scenarios. In this work a dynamic prediction scheme for these parameters is presented. This approach ensures an appropriate traffic differentiation while maintaining compatibility with the stations without QoS support. As the APs are the only devices that use this algorithm, no changes are required to current network cards. The results show improvements in both voice and video transmissions, as well as in the QoS level of the network that the proposal achieves with regard to EDCA.

The incessant development of High Quality (HQ) multimedia contents and the trend towards the use of wireless technologies have as a consequence the need for providing the users with an adequate level of Quality of Service (QoS) in IEEE 802.11 networks. The IEEE 802.11e amendment aims to overcome this situation by introducing the Enhanced Distributed Channel Access (EDCA) access method. This new method is characterised through a group of Medium Access Control (MAC) parameters, which are able to classify and prioritize the different types of traffic. In this regard, the most determining parameter is the Arbitration Inter-Frame Space Number (AIFSN). On this basis, we propose a new adaptation scheme that makes use of a M5 regression model with the aim of improving the voice and video performance offered by EDCA. Our proposal is able to determine dynamically the optimum AIFSN values with regard to the network conditions, maintaining the backward compatibility with the stations that use the original IEEE 802.11 standard. The prediction algorithm is only queried by the Access Point (AP), without introducing additional control traffic into the network, making it possible to use it in real-time. With respect to the standard EDCA values, the results show an enhancement in the voice+video normalized throughput and a significant reduction in the number of the retransmission attempts.

The original version of the IEEE 802.11 standard is not able to provide the required Quality of Service (QoS) for real-time applications. The IEEE 802.11e amendment was developed to overcome this situation, introducing the Enhanced Distributed Channel Access (EDCA) as a new channel access method. This method makes it possible to prioritize the different types of traffic through a group of Medium Access Control (MAC) parameters. The most important role of these MAC parameters is played by the Arbitration Inter-Frame Space Number (AIFSN). Although the AIFSN can be modified during a transmission, the commercial Access Points (APs) only use the combination defined in the standard. Therefore, we propose a new adaptation scheme for network traffic priorities involving the construction of a J48 decision tree classifier with the main goal of enhancing the voice and video communications. This classifier calculates a new set of AIFSN values by taking into account the current network conditions, maintaining the interoperability with the legacy Distributed Coordination Function (DCF) applications. The results show that our proposal improves upon the voice+video performance results obtained by the AIFSN standard values by up to 20%. Furthermore, this scheme is fully compatible with the commercial network cards available on the market.