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.

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.

In recent years, new management methods have appeared that mark the beginning of a new industrial revolution called Industry 4.0 or the Industrial Internet of Things (IIoT). IIoT brings together new emerging technologies, such as the Internet of Things (IoT), Deep Learning (DL) and Machine Learning (ML), that contribute to new applications, industrial processes and efficiency management in factories. This combination of new technologies and contexts is paired with Multi-access Edge Computing (MEC) to reduce costs through the virtualisation of networks and services. As these new paradigms increase in growth, so does the number of threats and vulnerabilities, making IIoT a very desirable target for cybercriminals. In addition, IIoT devices have certain intrinsic limitations, especially due to their limited resources, and this makes it impossible, in many cases, to detect attacks by using solutions designed for other paradigms. So it is necessary to design, implement and evaluate new solutions or adapt existing ones. Therefore, this paper proposes an intelligent threat detector based on boosted tree algorithms. Such detectors have been implemented and evaluated in an environment specifically designed to test IIoT deployments. In this way, we can learn how these algorithms, which have been successful in multiple contexts, behave in a paradigm with known constraints. The results obtained in the study show that our intelligent threat detector achieves a mean efficiency of between 95%–99% in the F1 Score metric, indicating that it is a good option for implementation in these scenarios.

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.

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.

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.

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.

Video communications require the use of networks capable of optimally allocating resources and adapting dynamically themselves to the changing operating conditions. When a video stream has to be delivered to various receivers, the appropriate service to accomplish the delivery process is the multicast communication service. In the particular case of the widely used IEEE 802.11 Wireless LANs (WLANs), the multicast service is simply specified as an unreliable broadcasting service, i.e., it does not include any error recovery mechanism. The absence of feedback from the receivers does not only prevent the sender from taking proper action to recover the corrupted packets, but it also makes unfeasible the implementation of a channel rate adaptation mechanism as the one used by the IEEE 802.11 unicast service. The absence of such mechanism may render the multicast service useless if the channel conditions are so that it makes unreliable the delivery process of the multicast traffic. Even though the IEEE has been working on an amendment to the standard specifying the operation of various error recovery mechanisms for the multicast service, these services have not been designed to support any rate adaptation mechanism. In this paper, we present a novel auto rate selection multicast mechanism, whose design is based on the collision prevention to increase the reliability. We have developed QoS and QoE evaluations via simulation, demonstrating that our proposal outperforms the video quality perceived by the end-user.

With the deployment of Audio-Video (AV) services and the flexibility offered by wireless networks, there is more and more an increasing interest on defining more reliable multicast mechanisms. Towards this end, the IEEE Task Group aa (TGaa) is currently working on the definition of the IEEE802.11aa amendment. The standardization efforts in this area aim to develop efficient QoS mechanisms by keeping in mind that any new mechanism should: 1) properly interoperate with the already existing unicast mechanisms; and 2) provide the robustness required by AV applications. In order to meet the former, the efforts are focusing on amending the existing multicast standard while to meet the latter there is a need of better understanding the stringent requirements of AV streaming applications. In this work, after reviewing the amendments introduced by the IEEE 802.11aa, we carried out an evaluation of the proposed multicast mechanisms when supporting video streaming applications. The main contribution of this work focuses therefore on evaluating the QoS parameters that the TGaa has taken into account to define the proposed mechanisms.

During the past few years, the development of video services over wireless networks has been driven by Quality of Service (QoS) parameters and service level agreements. Even though QoS parameters, such as packet losses, jitter and delays are good indicators of the service provided by the underlying network services to the applications, they fall short on providing a better measure of the Quality of Experience (QoE) to the end-user. In order to gain a better understanding of the benefits in terms of the QoE guarantees, it is essential to evaluate the effectiveness of the enabling protocol mechanisms. In this paper, we introduce a novel multicast service for the IEEE 802.11 WLAN standard. The proposed service combines a novel MAC scheme and layered video, guaranteeing a minimum acceptable video quality to all the members of the multicast group through the base layer. To avoid penalizing the users capable of receiving data at higher rates and the performance of an IEEE 802.11 WLAN, the enhancement layers are transmitted using a higher transmission rate. We show that by properly coupling the video and multicast control mechanisms, the QoS as well as the QoE requirements of the end-user can be guaranteed. The performance of the proposed system architecture is extensively investigated by simulations and through numerical evaluation of relevant QoS and QoE metrics. Furthermore, our results show that the real-time video streaming quality of the total system can be greatly improved while keeping downward compatibility with the IEEE 802.11 multicast mechanism.

Multicast is an efficient paradigm for transmitting data from a sender to a group of receivers. According to the IEEE 802.11 standard, the multicast service is defined as an unreliable service, that is, it does not include the use of ACK frames. Furthermore, different to the unicast service, the multicast service makes use of a single rate out of the various rates included in the basic service set defined by the IEEE 802.11 standard. Even though various proposals have recently appeared in the literature addressing these issues, none of them has come out with a structured set of control mechanisms taking into account the varying conditions characterising the wireless channels as well as the requirements of various applications. A novel cross-layer auto rate selection multicast mechanism for multi-rate wireless LANs, namely auto rate selection for multicast, capable of adapting the data transmission to the varying conditions of the channel and taking into account the characteristics of various applications, is introduced. The simulation results show that our proposal outperforms the IEEE 802.11 standard and the mechanisms recently proposed in the literature.