Edge intelligence combined with federated learning is considered as a way to distributed learning and inference tasks in a scalable way, by analyzing data close to where it is generated, unlike traditional cloud computing where data is offloaded to remote servers. In this paper, we address the placement of Artificial Intelligence Functions (AIF) making use of federated learning and hardware acceleration. We model the behavior of federated learning and related inference point to guide the placement decision, taking into consideration the specific constraint and the empirical behavior of a virtualized infrastructure anomaly detection use-case. Besides hardware acceleration, we consider the specific training time trend when distributing training over a network, by using empirical piece-wise linear distributions. We model the placement problem as a MILP and we propose a variant of the problem. Simulation results show the impact that hardware acceleration can have in the decision of the number of AIF to enable, while dividing by a relevant factor the distributed training time. We also show how our approach exacerbates the importance of monitoring an end-to-end learning system delay budget composed of link propagation delay and distributed training time in the location of AIFs.

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.

5G networks are expected to support numerous novel services and applications with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be reduced by moving the computational capability closer to the network edge. The limited amount of computational resources of the edge nodes, however, poses the challenge of efficiently utilizing these resources while, at the same time, satisfying QoS requirements. In this work, we employ mixed-integer linear programming (MILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, where SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks composed of decentralized units (DUs), centralized units (CUs), and a core network (5GC). Specifically, we compare four approaches to solving the problem. The first two approaches minimize, respectively, the E2E latency experienced by users and the service provisioning cost. The other two instead aim at minimizing VSF migrations along with their impact on users’ quality of experience with the last one minimizing also the number of inter-CU handovers. We then propose a heuristic to address the scalability issue of the MILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

Network softwarization is one of the key enablers of the future Internet evolution, also supporting the road from the fifth generation (5G) to the next-generation communication systems, namely 6G, with their main objective of bringing hyper-connected experience to every corner of society.

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.

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.

Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in 5G and beyond networks. However, adequate mechanisms are required to meet the dynamically changing network service demands to utilize the network resources optimally and also to satisfy the demanding QoS requirements. Particularly in multi-domain scenarios, the additional challenge of isolation and data privacy among domains needs to be tackled. To this end, centralized and distributed Artificial Intelligence (AI)-driven resource orchestration techniques (e.g., virtual network function (VNF) autoscaling) are foreseen as the main enabler. In this work, we propose deep learning models, both centralized and federated approaches, that can perform horizontal and vertical autoscaling in multi-domain networks. The problem of autoscaling is modelled as a time series forecasting problem that predicts the future number of VNF instances based on the expected traffic demand. We evaluate the performance of various deep learning models trained over a commercial network operator dataset and investigate the pros and cons of federated learning over centralized learning approaches. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging our MEC platform and assess the performance of the proposed deep learning models in a practical setup.

Due to the varying conditions in traffic and resource availability in networks nowadays, maintaining continuity of network service and satisfying QoS and QoE requirements became a challenging task. If considered in a highly diversified environment in terms of technology and administration, it gets even more complicated, and appropriate service and resource management solutions are mandatory. Thus, the aim of this paper is to present our specific perspective of the ongoing European H2020 5G-CARMEN project, addressing the importance of proactive reconfiguration of network services and their migration between different domains. Leveraging 5G technology and MEC platform, our management platform for automated low-latency-aware VNF placement and migration will enable orchestration of network services and resources across different administrative and technology domains.

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

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.

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.

5G networks are characterized by massive device connectivity, supporting a wide range of novel applications with their diverse Quality of Service (QoS) requirements. This poses a challenge since 5G as one-fits-all technology has to simultaneously address all these requirements. Network slicing has been proposed to cope with this challenge, calling for efficient slicing and slice placement strategies in order to ensure that the slice requirements (e.g., latency, data rate) are met, while the network resources are utilized in the most optimal manner. In this paper, we compare different end-to-end (E2E) slice placement strategies by formulating and solving a Mixed Integer Linear Programming (MILP) slice placement problem and study their trade-offs. E2E slice requests are modelled as Service Functions Chains (SFC), in which each core network and radio access network component is represented as a Virtual Network Function (VNF). Based on the analysis of the results, we then propose a slice placement heuristic algorithm whose objective is to minimize the number of VNF migrations in the network and their impact onto the slices while, at the same time, optimizing the network utilization and making sure that the QoS requirements of the considered slice requests are satisfied. The results of the simulations demonstrate the efficiency of the proposed algorithm.

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.

The 5th generation mobile network (5G) is expected to support numerous services with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be significantly reduced by moving content/computing capability closer to the network edge. However, since the edge nodes (i.e., base stations) have limited computing capacity, mobile network operators shall make a decision on how to provision the computing resources to the services in order to make sure that the E2E latency requirement of the services are satisfied while the network resources (e.g., computing, radio, and transport network resources) are used in an efficient manner. In this work, we employ integer linear programming (ILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, and resource allocation problem where SFCs, composed of virtualized service functions (VSFs), represent user requested services that have certain E2E latency and data rate requirements. Specifically, we compare three variants of an ILP-based algorithm that aim to minimize E2E latency of requested services, service provisioning cost, and VSF migration frequency, respectively. We then propose a heuristic in order to address the scalability issue of the ILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

In this article, we experimentally measure the throughput performance of a Wi-Fi, 802.11n, network when it is affected by LTE downlink transmissions. Our practical approach is based on a modular experimental test-bed. We initially compare our measurement results with the case without LTE interference; and further discuss that even the 3GPP features cannot guarantee coexistence in all cases and this might hamper the practicality of mobile technology in the unlicensed radio spectrum. For this reason, we enhance our test-bed introducing the Software-Defined Radio Access Network (SD-RAN) controller 5G-EmPOWER. Thus borrowing from the higher agility of software-defined networking. By using the SD-RAN control to adaptively tune LTE-eNB downlink transmission parameters, we experimentally prove the validity of this approach to improve Wi-Fi network throughput, as well as we shed light onto the new potentials that the SD-RAN controller can lead to automate network optimization.

Network Function Virtualization is a promising technology that proposes to decouple the network functions from their underlying hardware and transform them into software entities called Virtual Network Functions (VNFs). This approach offers network operators with more flexibility to instantiate, configure, scale, and migrate VNFs at runtime depending on the demand. On introducing these VNFs at the network edges (e.g., base stations), emerging use cases such as connected cars can be supported. However, in such an environment, efficient VNF placement and orchestration mechanisms are needed to address the challenges of continuously changing network dynamics, service latency requirements and user mobility patterns. The purpose of this paper is twofold. Firstly, we propose a neural-network model (i.e., a subset of machine learning) that can assist in proactive auto-scaling by predicting the number of VNF instances required as a function of the network traffic they should process. Based on the traffic traces collected over a commercial mobile network, the model achieves a prediction accuracy of 97%. Secondly, we provide an Integer Linear Programming formulation for placing these VNFs at the edge nodes with a primary objective of minimizing end-to-end latency from all users to their respective VNFs. Our results show an improvement in latency by upto 75% when VNFs are placed at the network edges.

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 function virtualization (NFV) will simplify deployment and management of network and telecommunication services. NFV provides flexibility by virtualizing the network functions and moving them to a virtualization platform. In order to achieve its full potential, NFV is being extended to mobile or wireless networks by considering virtualization of radio functions. A typical network service setup requires the allocation of a virtual network function-forwarding graph (VNF-FG). A VNF-FG is allocated considering the resource constraints of the lower infrastructure. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering also VNF migration. Then we formulate the optimization problem as an integer linear programming (ILP) and provide a heuristic algorithm for allocating multiple VNF-FGs. The idea is that VNF-FGs can be reallocated dynamically to obtain the optimal solution over time. First, a centralized optimization approach is proposed to cope with the ILP-resource allocation problem. Next, a decentralized optimization approach is proposed to deal with cooperative multi-operator scenarios. We adopt AD3, an alternating direction method of multipliers-based algorithm, to solve this problem in a distributed way. The results confirm that the proposed algorithms are able to optimize the network utilization, while limiting the number of reallocations of VNFs which could interrupt network services.

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.

Among the significant advances in mobile network technology, as evident in the latest 3GPP releases, one of the most notable is the possibility to do aggregation between licensed and unlicensed carriers. With LTE transmitting over unlicensed bands, obvious concerns of a fair co- existence with other preexisting technologies have risen up. In this study, we aim to evaluate the impact of LTE transmission on the key mechanism of Clear Channel Assessment (CCA), which is common to several unlicensed systems, amongst which Wi-Fi is the most notable. Relying on the statistical tool of stochastic geometry and a semianalytical approach, we will obtain the probabilities of Wi-Fi preamble false alarm and detection under a wide set of realistic propagation effects, such as path-loss and Rayleigh distributed fading. Above all, we will model the effect of a single LTE downlink interfering transmission, as well as the aggregate interference effect. Hence, we shall be able to evaluate the modified energy detection threshold that has been long debated between 3GPP and IEEE 802.11 Working Groups.

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.

WiFi networks are known to be a cost-efficient traffic offloading solution for mobile networks. The Multi Access Packet Data Network Connectivity is a feature introduced in LTE Release 10 in order to allow users to be simultaneously connected to multiple radio access networks (RAN). Although this feature brings many advantages, such as the possibility to implement QoS-based traffic steering, it poses also many challenges, one of which is distributing traffic among the two radio access technologies. In this paper, we propose a traffic- aware user association algorithm for heterogeneous LTE/WiFi RANs. The proposed algorithm is formulated as an Integer Linear Programming (ILP) problem jointly optimizing user association and resource allocation. A heuristic is also proposed in order to address the scalability issues of the ILP-based algorithm. Numerical simulations are used in order to compare the proposed approaches. Finally, we implemented and tested the heuristic in small-scale testbed using the 5G-EmPOWER platform.

Network Function Virtualization (NFV) has raised tremendous attention in the academic and industrial communities alike. The former have been attracted by the service centric orchestration challenges. The latter have found that, by decoupling network functions from the underling hardware, significant savings can be obtained by means of infrastructure homogenization and service automation. As opposed to the initial NFV solutions, which heavily relied on a centralized cloud model, the emerging Multi-access Edge Computing trend is calling for a distribution of computational capacity at the customers' sites. This change of paradigm could result in the deployment of tens of thousands or even millions of Points-of-Presence. In this article we discuss the fundamental challenges of deploying NFV in scattered environments, then we introduce the Light-MANO framework, a Multi-access Network Operating System converging SDN and NFV into a single lightweight platform for management and orchestration of network services over distributed NFV infrastructure. Finally, we report on a proof- of-concept implementation of LightMANO and on its evaluation.

5G networks will incorporate new innovative technologies and concepts such as network virtualization, SDN/NFV, multi-tenancy and network slicing. Moreover, resource orchestration play a pivotal role to dynamically deploy network services and allocate resources. Orchestration is a control function for resource management in the network core and centralized cloud infrastructure. However, for end-to-end slicing and resource management, the orchestration functions must also be realized at the network edge i.e., the RAN segment. In this paper, we present an architecture for active RAN resource orchestration in which radio resource allocation to different tenants is dynamically scaled in real-time. A parallel can be drawn to the classic spectrum sharing scenarios as we evaluate the well known co-primary sharing model in a multi-tenant RAN context. Moreover, we extend SimuLTE, a well-known system level simulation model to integrate our RAN orchestration architecture and implement different scheduling policies. We evaluate system level network performance using fine-grained radio resource sharing approach and evaluate the involved performance-fairness trade-offs.

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.

Network Function Virtualization (NFV) is deemed as a mean to simplify deployment and management of network and telecommunication services. With wireless access networks, NFV has to take into account the radio resources at wireless nodes in order to provide an end-to-end optimal virtual network function (VNF) allocation. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering VNF migration. Then we formulate the optimisation problem as an Integer Linear Programming (ILP) and provide a heuristic algorithm for allocating multiple service function chains (SFCs). The proposed approach allows SFCs to be reallocated so as to obtain the optimal solution over time. The results confirm that the proposed algorithm is able to optimize the network utilization while limiting the reallocation of VNFs which could interrupt services.

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.

5G networks are expected to support various applications with diverse requirements in terms of latency, data rates, and traffic volume. Cloud-RAN(C-RAN) and densely deployed small cells are two of the tools at disposal of mobile network operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the C-RAN architecture, several functional splits, each characterized by a different demarcation point between the centralized and the distributed units, have emerged. However, the selection of the appropriate centralization level (i.e., the functional split) still remains a challenging task, since a number of parameters have to be considered in order to make such a decision. In this paper, a virtual network embedding (VNE) algorithm is proposed to flexibly select the appropriate functional split for each small cell. The VNE is formulated as an integer linear programming (ILP) problem whose objective is to jointly minimize the inter-cell interference and the fronthaul bandwidth utilization by dynamically selecting the appropriate functional split. Specifically, dynamic and static ILP-based algorithms are proposed. Finally, dynamic and static VNE heuristics are proposed to address the scalability problem of the ILP-based algorithms in case of dense and ultra-dense mobile networks, respectively.

By leveraging the fully-centralized and virtualized cloud radio access network (C-RAN) architecture over densely deployed small cells, mobile network operators (MNOs) are expected to meet the ever-increasing coverage and capacity demands. Towards this end, finding the optimal numbers, and locations of centralized unit (CU) pools, and centralizing the baseband units of eNBs at the optimal CU pools plays a pivotal role in curtailing the required investments in order to transit from legacy decentralized RAN (D-RAN) to C-RAN. In this paper, we propose an approach for MNOs to adopt the C-RAN architecture with minimal investment by using the available infrastructure (e.g., site locations and transmission links). Specifically, we propose a decentralized unit - CU (DU-CU) mapping algorithm, which effectively selects the quantity and the locations of CU pools and assigns the CU of each DU to the appropriate CU pool. We then compare the traffic aggregation gains of C-RAN and traditional D-RAN. Lastly, in order to quantify the total cost of ownership savings that can be obtained by employing the legacy network infrastructure while migrating to C-RAN, we compare this scenario with the C-RAN migration scenario in which there is no available infrastructure. In both scenarios, the mapping algorithms are formulated as virtual network embedding problems using integer linear programming techniques. The results of the simulations, conducted using data traffic of 26 eNBs (209 cells) of an operational LTE-A mobile network, reveal that significant saving can be obtained by employing the available mobile network infrastructure while migrating to C-RAN.

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.

Network Function Virtualization (NFV) is a promising technology that is receiving significant attention in both academia and the industry. NFV paradigm proposes to decouple Network Functions (NFs) from dedicated hardware equipment, offering a better sharing of physical resources and providing more flexibility to network operators. However, in such environment, efficient management mechanisms are crucial to address the problem of Placement and Chaining of Virtual Network Functions (PC-VNF). In this paper, we introduce a PC-VNF model based on a flexible resource allocation approach that takes into account service requirements in terms of latency, in addition to traditional connectivity and resource utilization. This is particularly important for emerging 5G services such as ultrareliable, low latency and massive machine type communications. The end-to-end performance needs to meet the user expectations as well as service requirements to provide the desired QoS/QoE. Our main goal is to determine the optimal VNF placement minimizing resource consumption while providing specific latency (i.e., end-to-end delay) and avoiding violation of Service Level Agreements (SLA) by constraining allocated resources to a given VNF to reach its required performance. Results show that our approach achieves the required latency with better resources utilization compared to the classical approaches, with a reduction of up to 40% of resource consumption and a higher rate of accepted requests by recovering 15 to 60 % of the rejected requests.

5G networks are expected to support various applications with diverse requirements in terms of latency, data rates and traffic volume. Cloud-RAN and densely deployed small cells are two of the tools at disposal of Mobile Network Operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the Cloud-RAN architecture, several functional splits, each characterized by a different demarcation point between the centralized and the distributed units, have emerged. However, the selection of the appropriate centralization level (i.e., the functional split) still remains a challenging task, since a number of parameters have to be considered in order to make such a decision. In this paper, a virtual network embedding (VNE) algorithm is proposed to flexibly select the appropriate functional split. The VNE is formulated as an Integer Linear Programming problem whose objective is to minimize the intercell interference and the fronthaul bandwidth utilization by dynamically selecting the appropriate functional split. Finally, a scalable VNE heuristic is also proposed.

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 orchestration of network services is a well investigated problem. Standards and recommendation have been produced by ETSI and IETF while a significant body of scientific literature can be found exploring both the theoretical and practical aspects of the problem. Likewise several open-source as well as proprietary tools for network service orchestration are already available. Nevertheless, in most of these cases network services can only be provisioned across a single administrative domain effectively preventing end-to-end network service delivery across multiple Infrastructure Providers (InP). In this paper we present X-MANO, a cross-domain network service orchestration framework consisting in an inter-domain confidentially-presenting federation interface and in an information model for multi-domain network service life-cycle programmability. X-MANO is effectively deployment-agnostic and can be used in hierarchical, peer-to-peer and cascading (or recursive) configuration. We validate X-MANO trough a proof-of-concept implementation over a multi-domain testbed. Finally, we release all the code under a permissive APACHE 2.0 license making it available to researchers and practitioners.

In the recent years several proprietary as well as open-source frameworks for network service orchestration have emerged. Similarly a significant body of literature has been produced investigating both the theoretical and practical aspects of network service orchestration. Nevertheless, currently available frameworks for network service orchestration are designed under the assumption that they can have full control over the resources being orchestrated. Such assumption does not hold any more when there is the need to pool both physical and virtual resources across different technological and administrative domains in order to deploy a service. In such a scenario, the different infrastructure providers may be reluctant to provide full access to their resources to a third party. In this paper we present X-MANO, a cross-domain network service orchestration framework. X-MANO is effectively deployment-agnostic and can be used in hierarchical, peer-to-peer and cascading (or recursive) configuration. We validate X-MANO through a proof-of-concept implementation over a multi-domain testbed. Finally, we release all the code under a permissive APACHE 2.0 license making it available to researchers and practitioners.

An important target for 5G networks is to enable resource sharing among network tenants such as Mobile Virtual Network Operators and Service Providers. Several domains of resource sharing have been considered including infrastructure (compute, storage and networking), transport, Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum sharing are expected to be an integral part of a multi-tenant 5G network. In this paper, a centralized, fine-grained active RAN and spectrum sharing approach has been presented and analyzed using a modified SimuLTE model. The presented model can be used for analyzing active RAN and spectrum sharing models considered in a multi-tenant 5G network. We present the core modules that enable dynamic allocation of RAN slices with dedicated spectrum and resource scheduling functions. We also present preliminary simulation results that give an insight into the actual benefits and trade-offs of active spectrum sharing among RAN tenants at different time-frequency granularities.

IEEE 802.11 (Wi-Fi) networks' popularity has boomed in recent years and their presence is continuously increasing. More and more large-scale networks are being deployed, consisting of a large number of access points. In these networks, the handover process has proven to be very challenging. In standard Wi-Fi, the client is responsible for making the handover decision and the access point plays no role in it. This can lead to several issues such as short stays, oscillating behaviour and poor overall connectivity. In this paper, we propose a way to shift the handover process from client to access point, without requiring any modification to the client itself. By using network virtualization, we are able to perform proactive and transparent handovers, steered by a centralized controller. This allows us to exploit valuable information such as the clients' distance from all available access points, leading to a better handover process. In this paper, we present several distance aware handover algorithms. The results show that we are able to improve the handover algorithm using this information by reducing the number of handovers and avoiding unnecessary ones. We were able to reduce the throughput penalty during a handover by 50% which leads to a more seamless handover process.

Softwarization of networks is an important trend, enabled by the NV (Network Virtualization), SDN (Software-Defined Networking), and NFV (Network Function Virtualization) paradigms and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

Small-cells are rapidly emerging as the mobile operators' choice to provide additional capacity in current and future mobile networks. However, in order to fully deliver on their promises, small-cells need to address severe interference control and coordination challenges. By centralizing base-band processing in large high-volume computing infrastructures, Cloud-RAN can effectively enable advanced coordination features for dense small-cells deployments. Unfortunately, Cloud-RAN tight bandwidth and latency requirements have made optical fiber the most common solution for the links interconnecting remote radio heads (RRHs) with the base band units (BBUs), i.e. the fronthaul. Recent advances in microwave communications are making wireless fronthauls a viable option especially in dense urban environments where fiber fronthauls could be too rigid for accommodating highly dynamic traffic patterns. In this paper, we provide a novel formulation for the BBU Placement problem where BBU pools are placed at the edges of the network, possibly co-located with macro-cells, and a reconfigurable wireless fronthaul is used in order to provide RRHs with connectivity. To the best of our knowledge this is the first work to tackle the BBU placement problem over a reconfigurable substrate network with mmWave links. We also propose a BBU Placement heuristics, and we evaluate it using a numerical simulator.

Intent-based networking is a major component that will transform the manner in which the SDN/NFV-enabled future network infrastructures are operated. In particular, Intent-based networking is expected to play a major role in the multi-technological and software-defined 5G systems development roadmap. In this paper, we present the design and prototype implementation of an Intent-based mobile backhauling interface for 5G networks. Finally, we report on the empirical evaluation of of the proposed Intent-based interface over a small Enterprise WLAN. We also release the entire software stack under a permissive license for academic use.

RINA, the Recursive InterNetwork Architecture, is a novel “back to basics” type approach to networking. The recursive nature of RINA calls for radically different approaches to how networking is performed. It shows great potential in many aspects, e.g. by simplifying management and providing better security. However, RINA has not been explored for congestion control yet. In this paper, we take first steps to investigate how congestion control can be performed in RINA, and demonstrate that it can be very efficient because it is applied close to where the problem happens, and through its recursive architecture, interesting effects can be achieved. We also show how easily congestion control can be combined with routing, enabling a straightforward implementation of in-network resource pooling.

Traditionally, network management tasks manually performed by system administrators include monitoring alarms based on collected statistics across many heterogeneous systems, correlating these alarms to identify potential problems or changes to management policies and responding by performing system re-configurations to ensure optimal performance of network services. System administrators have a narrow focus of factors impacting network service provisioning and performance due to the heterogeneity and scale of generated underlying network events. However, self-adaption principles are conceptual approaches for autonomously managing such complex distributed systems. Network management systems that harness such principles can dynamically and autonomously optimise the operation of network services, responding quickly to changes in user requirements and underlying network conditions. In this paper, we present a novel self-adaptive network management framework that takes advantage of a recursive network architecture for a simpler and more comprehensive application of ontologies, semantic web rules and machine learning to automatically adjust network configuration parameters to provide more optimal QoS management of network services. We demonstrate the applicability of the approach using a content distribution network (CDN) operating over such a recursive network architecture.

Network Function Virtualization (NFV) is set to disrupt the current networking ecosystem by turning vertically-integrated middleboxes into software modules running on general purpose virtualized platforms. NFV will play a key role in future wireless and mobile networks where significant cost reductions can be obtained by virtualizing different layers and functions of the radio access and core network. Such goal raises several challenges in terms of both functional decomposition of the radio nodes and for the management and orchestration of the resulting network. In this work we present Scylla a high-level declarative language for programming network functions that allows programmers to implement per-flow custom packet processing. We also introduce a set of programming abstractions modeling the fundamental aspects of VNF orchestration. Finally, we present a proof-of-concept Controller and an SDK implementing the proposed abstractions.

Small cell networks are key components in 5G networks to boost the network capacity, improve spectrum and energy efficiency, and enable flexible and new services. Due to the flexible spectrum access among and flexible deployment of small cells, the inter- cell coordination becomes critical for the performance of the network. In this paper, based on the key concept in software defined networking (SDN) for Internet, we first introduce the network graph approach as a tool for the control and coordination among small cells. The network graph is constructed from the abstracted network state information extracted from underlying base stations. It shields the logical centralized control unit from implementation details of the underlying physical layer and thus reduces the control overhead in a centralized solution. We use the network graph for network energy saving in small cell networks, in which network graphs are used to decide the optimal set of small cells in the network. For cells outside this set we can switch them off for energy saving. We propose three types of network graphs with different network state details. Based on these graphs, we formulate the energy saving problem as an integer linear programming (ILP) problem, and propose the practical algorithms to solve the problem. The performance of the algorithms are studied by simulation. It shows the potential of the proposed network graph approach for the inter-cell resource coordination in small cell networks.

Key features of satellite communications such as wide-scale coverage, broadcast/multicast support and high availability, together with significant amounts of new satellite capacity coming online, anticipate new opportunities for satellite communications services as an integral part within upcoming 5G systems. To materialize these opportunities, satellite communications services have to be provisioned and operated in a more flexible, agile and cost-effective manner than done today. In this context, this paper describes a solution for the virtualization and dynamic orchestration of satellite communication services that builds on the introduction of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies within the satellite ground segment systems. Along with the description of the main system architecture traits, the flowchart of a general procedure for the dynamic instantiation of virtualized satellite networks on top of a SDN/NFV-enabled satellite ground segment system is provided. The paper also presents experimental results for the dynamic customization of satellite network services through the implementation of a set of virtualized satellite network functions that can be orchestrated over general purpose open virtual platforms.

Network function virtualization (NFV) sits firmly on the networking evolutionary path. By migrating network functions from dedicated devices to general purpose computing platforms, NFV can help reduce the cost to deploy and operate large IT infrastructures. In particular, NFV is expected to play a pivotal role in mobile networks where significant cost reductions can be obtained by dynamically deploying and scaling virtual network functions (VNFs) in the core network. However, in order to achieve its full potential, NFV needs to extend its reach also to the radio access segment. Here, mobile virtual network operators shall be allowed to request radio access VNFs with custom resource allocation solutions. Such a requirement raises several challenges in terms of performance isolation and resource provisioning. In this work, we formalize the wireless VNF placement problem in the radio access network as an integer linear programming problem and we propose a VNF placement heuristic, named wireless network embedding (WiNE), to solve the problem. Moreover, we present a proof-of-concept implementation of an NFV management and orchestration framework for enterprise WLANs. The proposed architecture builds on a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing capable nodes.

To cope up with the booming of data traffic and to accommodate new and emerging technologies such as machine-type communications, the 5th Generation (5G) of mobile networks must be empowered with efficient resource allocation schemes that benefit from the adoption of the Software-Defined networking (SDN) paradigm. In radio communications, allocation of resources is tightly connected with interference. In this paper, we revisit the way wireless interference is managed and avoided relying on the SDN paradigm for controlling the network. The SDN approach is exploited to expose the lower layers of the stack (e.g., Physical and Medium Access Control) to the controller and its applications by making system parameters available, such that it is possible to dynamically configure the network in a logically centralized fashion, by means of specifically designed algorithms. The contribution of this work is threefold. First, we show how to adapt the SDN paradigm to mobile networks. Second, we propose the interference graph as an abstraction that can be used to control interference. Last, we formulate a throughput optimization tool that uses the proposed interference graph as an input.

Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.

Software-Defined Networking is gaining increasing interest in the academic and the industrial communities alike. SDN principles call for commodity networking devices and for shifting all intelligence to a logically centralized controller. In this demo we build on a programmable enterprise WLAN platform in order to implement and deploy a location analytics solution to be used in shopping malls, airports, and similar venues for location-based advertisement and visitors profiling. Our solution can run on commodity devices and in many cases can be adapted to run over an existing WLAN infrastructure.

Software-Defined Networking promises to deliver more flexible and manageable networks by providing a clear decoupling between control plane and data plane and by implementing the latter in a logically centralized controller. However, if such principles are to be applied also to wireless networks, new primitives and abstractions capable of providing programmers with a global view of the network capturing channel quality and interference must be devised. Moreover, the dynamic radio environment necessitates fast adaptation of physical parameters such as power, modulation and coding schemes. So the wireless SDN abstractions should allow for such adaptations to happen closer to the air interface. In this paper, we present high level abstractions for channel quality, interference and network reconfiguration; the latter permits operations differing in timescales to be carried out at different controller entities. The proposed concepts have been implemented and evaluated over a WiFi-based WLAN. Empirical measurements show that the proposed platform can be used to implement typical WiFi network management tasks such as channel assignment and interference monitoring.

Network Function Virtualization (NFV) has recently been proposed as a tool to optimize the deployment of network functions by shifting the processing from dedicated middleboxes to general purpose and inexpensive hardware platforms. In this paper, we propose a novel NFV-based management and orchestration framework for enterprise WLANs. Our framework is compatible with the ETSI NFV architecture and leverages on hybrid nodes combining the forwarding capabilities of a programmable switch with the storage/computational capabilities of a server. We propose an algorithm for Virtual Network Function placement which optimizes the functions deployment according to application level constraints. A proof-of-concept implementation of the proposed framework and a preliminary performance evaluation of selected VNFs are also presented.

No abstract available.

The quickly growing demand for wireless networks and the numerous application-specific requirements stand in stark contrast to today's inflexible management and operation of WiFi networks. In this paper, we present and evaluate OpenSDWN, a novel WiFi architecture based on an SDN/NFV approach. OpenSDWN exploits datapath programmability to enable service differentiation and fine-grained transmission control, facilitating the prioritization of critical applications. OpenSDWN implements per-client virtual access points and per-client virtual middleboxes, to render network functions more flexible and support mobility and seamless migration. OpenSDWN can also be used to out-source the control over the home network to a participatory interface or to an Internet Service Provider.

Software-Defined Networking (SDN) has received, in the last years, significant interest from the academic and the industrial communities alike. The decoupled control and data planes found in an SDN allows for logically centralized intelligence in the control plane and generalized network hardware in the data plane. Although the current SDN ecosystem provides a rich support for wired packet-switched networks, the same cannot be said for wireless networks where specific radio data-plane abstractions, controllers, and programming primitives are still yet to be established. In this work, we present a set of programming abstractions modeling the fundamental aspects of a wireless network, namely state management, resource provisioning, network monitoring, and network reconfiguration. The proposed abstractions hide away the implementation details of the underlying wireless technology providing programmers with expressive tools to control the state of the network. We also present a Software-Defined Radio Access Network Controller for Enterprise WLANs and a Python--based Software Development Kit implementing the proposed abstractions. Finally, we experimentally evaluate the usefulness, efficiency and flexibility of the platform over a real 802.11-based WLAN.

Programming wireless networks requires accounting for multiple complex operations, such as monitoring interference and allocating radio resources. Employing the Software-Defined Networking (SDN) paradigm eases the implementation of such tasks when augmented with suitable high-level programming abstractions. In this work, we present a set of programming abstractions modeling three fundamental aspects of a wireless networks, namely state management, resource provisioning, and network state collection. We also describe our proof-of-concept implementation of the proposed abstractions focusing on WiFi networks and show its use for realizing typical control tasks such as mobility management and traffic engineering as Network Apps.

In this demo we present Joule, a software enabling real-time power consumption monitoring of networking devices without having to deploy physical meters in the form of, for example, managed Power-Over-Ethernet supplies. We will demonstrate how Joule can estimate the power consumption of commercial WiFi Access Points by exploiting observable network statistics and under different workloads, e.g. browsing, video streaming, and background FTP traffic. An interactive web dashboard will allow attendees to have a first hand experience on the correlation between traffic and power consumption.

Past years have witnessed the surge of mobile broadband Internet traffic due to the broad adoption of a number of major technical advances in new wireless technologies and consumer electronics. In this respect, mobile networks have greatly increased their availability to meet the exponentially growing capacity demand of modern mobile applications and services. The upcoming scenario in the near future lays down the possibility of a continuum of communications thanks also to the deployment of so called small cells. Conventional cellular networks and the small cells will form the foundation of this pervasive communication system. Therefore, future wireless systems must carry the necessary scalability and seamless operation to accommodate the users and integrate the macro cells and small cells together. In this work we propose the V-Cell concept and architecture. V-Cell is potentially leading to a paradigm shift when approaching the system designs that allows to overcome most of the limitations of physical layer techniques in conventional wireless networks.

Network virtualization sits firmly on the Internet evolutionary path allowing researchers to experiment with novel clean-slate designs over the production network and practitioners to manage multi-tenants infrastructures in a flexible and scalable manner. In such scenarios, isolation between virtual networks is often intended as purely logical: this is the case of address space isolation or flow space isolation. This approach neglects the effect that network virtualization has on resource allocation network-wide. In this work we investigate the price paid by a purely logical approach in terms of performance degradation. This performance loss is paid by the actual users of a multi-tenants datacenter network. We propose a solution to this problem leveraging on a new network virtualization primitive, namely an online link utilization feedback mechanism. It provides each tenant with the necessary information to make efficient use of network resources. We evaluate our solution trough a real implementation exploiting the OpenFlow protocol. Empirical results confirm that the proposed scheme is able to support tenants in exploiting virtualized network resources effectively.

Reliable power consumption metering is important for power consumption management and optimization in wireless access networks. As a matter of fact, it is estimated that by 2015 wireless access networks will account for about 90% of the entire wireless energy cloud footprint. This can be traced back to the massive deployment of WiFi hotspots in the recent years in order to partially offload cellular networks from the load generated by modern data hungry mobile applications. In such a scenario the first step in reducing the power consumption of any IT infrastructure is to acknowledge it. Production-level solutions for large-scale monitoring campaign do exist, in the form, for example, of managed Power Over Ethernet devices. These solutions are however expensive to install and to manage especially for large legacy deployments. To overcome this limitation we propose Joule a virtual power consumption monitoring solution capable of estimating in real-time the actual power consumption of WiFi Access Points. Field trials performed using various benchmarks and applications show that Joule can provide a precise estimation of the power consumed by a typical IEEE 802.11 Access Point.

Energy efficiency is acknowledged as a pivotal issue for a sustainable development of wireless networking technologies. Traditionally, most works in the area focused on the user equipment, where battery duration represents a key asset. However, as the smartphone and tablet revolution fuels a massive deployment of wireless networks, often in the form of WiFi hotspots, more and more attention is expected to be devoted to the energy-efficient management of wireless access infrastructure. These networks tend to be dense and over-provisioned, which in time leads to significant energy wastage in off-peak conditions. In this paper, we present MORFEO a flexible energy-saving decision algorithm to tune the energy consumption of a wireless infrastructure to the actual network conditions in terms of both user density and traffic patterns. Experimental results from a real-life deployment shows that our solution can deliver significant energy savings with minimal degradation in terms of the quality of service provided.

The energy wasted in wireless networks is a serious concern and the main challenge lies in determining when and where the energy is wasted. In this demo, we present Energino, an energy measurement and control system designed to deliver high performance while remaining a cheap solution.

Wireless Sensor Networks (WSNs) provide an extensible and effective means to monitor large and geographically diverse areas. Nodes in a WSN are characterized by very strict constraints in terms of computing power and energy consumption, as a result of which efficient and secure data aggregation techniques are being proposed as enabling technology for reducing network load, while at the same time providing high sensing accuracy and data integrity. In this paper, we present an hybrid mesh/sensor network architecture based on a sharing of tasks between mesh routers and sensor nodes. Our architecture is particularly suitable to realize an application agnostic mesh backhaul, able to concurrently support multiple WSNs while ensuring both end-to-end encryption and hop-by- hop authentication. Simulation analyses have shown that the proposed scheme can significantly reduce the network load while preserving data confidentiality and integrity. Finally, a real-world prototype has been implemented and tested over a small scale testbed confirming the simulation results.

Multi-hop wireless systems represent a viable means for deploying access networks covering medium-size areas with limited investment, making use of commodity hardware and freely available software suites. At the same time, management of such networks represent an overly complex task, due to the joint effect of the time-varying nature of the radio channel, user mobility and the inherently distributed nature of the system. A number of solutions are currently being researched, whereby network management functionalities get embedded within the wireless network itself simplifying both the initial network deployment and its subsequent maintenance by removing the need for a dedicated network controller which can be expensive in that it requires dedicated hosting facilities, power, and adequate cooling. A common building block of such approaches is represented by a monitoring framework able to bring the relevant network-level information to the decision points. In this paper, we present a distributed network monitoring toolkit, specifically developed for wireless multi-hop networks. The toolkit allows network administrators to monitor the status of the network as well as to plan and execute active measurement campaigns. Information is stored in a distributed network-wide repository and is accessible through a web interface.

Multi-hop wireless networks are emerging as a viable alternative for building access networks in areas where conventional solutions (cellular, fiber) are neither feasible nor attractive from an economical standpoint. The management of such networks represents an overly complex task because of the time-varying nature of the radio channel, the mobility of users and the presence of adaptive, self-configuration features. Various solutions are currently being researched, whereby network management functionalities are performed autonomously at the network nodes themselves. Such approaches require a monitoring framework able to bring network-level information to the relevant decision points in an effective and robust manner. In this paper, we present a distributed network monitoring framework, specifically developed for wireless multi-hop networks. The system architecture and the protocols are presented together with results obtained using a prototypical implementation over a real-world testbed. Experimental results show that the framework generates a limited amount of traffic, and that the system is able to consistently recover from node failures.

In this paper, we present the Airtime Deficit Round Robin (ADRR), a novel scheduling algorithm for IEEE 802.11-based wireless networks. The ADRR mechanism enhances the Deficit Round Robin scheduling discipline by taking into account the channel quality experienced by the transmitting node. The devised algorithm addresses the IEEE 802.11 performance anomaly, preventing a node which experiences poor channel conditions from monopolizing the wireless medium, lowering the performance of the whole system. The proposed approach combines link scheduling with measurable routing metrics. Simulation analyses have shown that the proposed scheme can achieve performance isolation among links characterized by heterogeneous channel conditions. A real prototype has been implemented and evaluated over a small scale testbed confirming the simulation results.

We show how the TUNEGreen real-time energy consumption monitoring capabilities can be used to understand how and where power is consumed in a simple star-shaped network based on IEEE 802.11. Different traffic patterns will be generated in order to demonstrate the correlation between traffic and energy consumption.

There is an increasing acceptance for Wireless Mesh Networks (WMN) as the potential `last-mile' access technology running media-rich applications with stringent quality of service (QoS) requirements. As WMNs are envisioned to provide high bandwidth broadband services to a large community of users, the Internet Gateway which acts as a central point of attachment for the mesh routers is likely to be a potential bottleneck because of its limited wireless link capacity. We propose TBMGA (Tree-Based with Multi-gateway Association), a novel routing protocol that elegantly balances the load among the different Internet gateways in a WMN. With TBMGA, we combine the flexibility of layer-2 routing with the self-configuring and self-healing capabilities of MANET routing. TBMGA switches the point of attachment of an active source-serviced gateway depending on a global metric estimated based on the average queue length and expected availability at the Internet gateway. The protocol is evaluated using simulations and we observe that the proposed scheme is able to efficiently balance the traffic between multiple gateways.

Wireless mesh networks are emerging as a promising access network architecture. In this work, we report on an extensive measurement campaign performed over an experimental WiFi-based mesh network, deployed in an office building environment. The measurements are focused on the performance of HTTP traffic. Results demonstrate the existence of an unfairness issue, whereby longer sessions achieve better performance than shorter ones. This is known to be potentially harmful in terms of mean session delay and user-perceived quality of service. Potential countermeasures are presented and discussed.

In this paper we present airtime deficit round robin (ADRR) a novel scheduling algorithm for IEEE 802.11-based wireless mesh networks. ADRR enhances the deficit round robin scheduling discipline by taking into account the channel quality experienced by the transmitting node. The devised algorithm addresses the IEEE 802.11 performance anomaly, preventing a node which experiences poor channel conditions from monopolizing the wireless medium lowering the performance of the whole system. The proposed approach combines link scheduling with measurable routing metrics typically available in WMNs. Results show the ability of the ADRR scheduler to achieve performance isolation among links characterized by heterogeneous channel conditions. The proposed solution has been implemented and tested over an IEEE 802.11-based wireless mesh network. Source code has been released under a BSD License making it fully available to the research community.