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The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators’ decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players’ revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.

Publisher Copyright: CCBY Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Deep generative models have increasingly become popular in different domains such as image processing, though, they hardly appear in the cybersecurity arena. While the main application of these models is dimensionality reduction, marginally they have been utilized for overcoming challenges such as data generalization and overfitting issues inherited from feature selection methods. To solve the mentioned challenges, we propose a combined architecture comprising a Conditional Variational AutoEncoder (CVAE) and a Random Forest (RF) classifier to automatically learn similarity among input features, provide data distribution in order to extract discriminative features from original features, and finally classify various types of attacks. CVAE introduces the labels of traffic packets into a latent space in order to better learn the changes of input samples and distinguish the data characteristics of each class. It avoids the confusion between classes while learning the whole data distribution. Compared with featureselection mechanisms such as Support Vector Machine Online (SVMo) by considering various evaluation metrics, the proposed architecture demonstrates considerable improvement in terms of performance. To verify the versatility of the proposed architecture, two publicly available datasets have been used in experiments.

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature to well compute the flow tables. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed.

The availability of network softwarization and virtualization technology in the field of telecommunications has opened the door to a radical review of the applications, protocols, and deployment models. In this evolving framework, old assumptions and constraints specific to satellite communications must be carefully re-assessed. To this aim, we revisit the role of the performance enhancing proxy (PEP), replaced by a chain of custom virtual network functions properly enabled to optimize common web traffic performance over a backhaul dynamically enabled with a supplementary satellite link. The resulting virtual PEP (vPEP) is compliant with the breakthrough virtualization and slicing paradigms and can fruitfully exploit the advanced features of the most recent IETF technologies such as QUIC and MPTCP.

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time-critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter-dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB-D simulation environment, along with a representative multi-traffic, multi-channel, multi-protocol communication system model, developed in the network simulator (ns-3). Second, a hierarchical engine for large-scale infrastructure co-simulation framework is developed to co-simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use-cases. While the example use-cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

Recently, the mobile traffic demand is increasing due to the drastic growth in the number of connected devices and smart services utilizing wireless cellular networks. In order to deal with this tough challenge, a key technology in 5G cellular networks, Multi-Access Edge Computing (MEC) which can provide ultra-low end-to-end latency, significant reduction of traffic load on backhaul, etc., has come into sight. The potential benefits of MEC in 5G and beyond have been revealed by plenty of state-of-the-art works. However, it leaves a difficult problem for the operators, which is to determine whether and how to deploy MEC in cellular networks due to the uncertainty of feedback from the investment on MEC. Although, there were a few works studying on the optimization of the number of MEC from the viewpoint of telecom operators, their contribution focus only on telecom operators rather than taking other operators (backhaul owners, Cloud owners, etc.) into account. In this paper, we propose a social maximization revenue model with the investment strategy for the telecom operators who decide the number of MEC and the backhaul owner who lease the backhaul capacity. The numerical results indicate that the optimal number of MEC and backhaul capacity that maximize the two players' profit is the same. Hence, it turns out that the advantage of MEC can be highlighted by using both Cloud and edge resource in parallel rather than processing all traffic on the MEC side.

Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the radio access network (RAN) architecture and offers degrees of design freedom. Yet, it also creates a challenging problem on how to design the functional split configuration. In this paper, a deep reinforcement learning approach is proposed to optimize function splitting in vRAN. A learning paradigm is developed that optimizes the location of functions in the RAN. These functions can be placed either at a central/cloud unit (CU) or a distributed unit (DU). This problem is formulated as constrained neural combinatorial reinforcement learning to minimize the total network cost. In this solution, a policy gradient method with Lagrangian relaxation is applied that uses a stacked long short-term memory (LSTM) neural network architecture to approximate the policy. Then, a sampling technique with a temperature hyperparameter is applied for the inference process. The results show that our proposed solution can learn the optimal function split decision and solve the problem with a $0.4\%$ optimality gap. Moreover, our method can reduce the cost by up to $320\%$ compared to a distributed-RAN (D-RAN). We also conclude that altering the traffic load and routing cost does not significantly degrade the optimality performance., This paper has been accepted in IEEE International Conference on Communications Workshops (ICC Workshops) 2021

5G offers high flexibility at the radio, transport and core networks to support various services of critical verticals such as connected and automated driving. At the Radio Access Network (RAN), 5G defines a New Radio (NR). 5G NR utilizes different subcarrier spacing, slot durations, modulations and channel coding schemes. This flexibility offers the possibility to support automotive services with different and demanding requirements, such as Advanced Driver-Assistance System (ADAS), cooperative driving, and remote driving. Previous studies showed that 5G NR can be configured to achieve latencies below 2 ms. However, existing studies are generally restricted to scenarios with a limited number of users and unlimited bandwidth. Therefore, it is important to analyze whether 5G NR can effectively support these services as the network scales under limited spectrum allocations. This study advances the current state of the art to demonstrate that the capability of 5G NR RAN to support advanced V2X services depends on the RAN configuration (subcarrier spacing, slot duration and error protection) and network load UMH work was supported in part by the Spanish Ministry of Science and Innovation (MCI), AEI and FEDER funds under Project TEC2017-88612-R, and the Ministry of Universities (IJC2018-036862-I), and the Generalitat Valenciana

Software-defined networking (SDN) and disaggregation are key technologies to fulfill the requirements of 5G and beyond optical metro networks. Indeed, open networking solutions will reduce vendor lock-in, achieving increased flexibility and programmability of the network. SDN-enabled sliceable bandwidth/bitrate variable transceivers (S-BVTs) are proposed to be adopted in metro networks in order to provide sustainable capacity growth and hyper-scalability, while enabling disaggregation and network flexibility/reconfigurability. We propose a transceiver modular approach following a pay-as-you grow model, which allows the S-BVT to be adapted to the traffic demand and network requirements. In fact, the S-BVT solution can be seen as a set of bandwidth/bitrate variable transceivers (BVTs) that can be pluggable and multi-vendor. Hence, SDN agents have been developed based on an OpenConfig vendor-neutral data model in order to efficiently reconfigure the S-BVTs fostering network agility and adaptability. Specifically, an optimized agent implementation is experimentally validated in order to increase the network efficiency and reduce the setup configuration time of an optical channel. Two slices/BVTs of 50 Gb/s each are successfully enabled and programmed to establish a high capacity flow of 100 Gb/s. The cost/power consumption of the proposed solution, based on direct detection (DD), is analyzed showing significant savings compared to 100 Gb/s coherent (CO) technology. This work was supported in part by the European Commission through the H2020-ICT-2016-2 METRO-HAUL Project under Grant G.A.761727 and in part by the Spanish Ministry MINECO Project AURORAS under Grant TEC RTI2018-99178-B-I00.

LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to ten years on a single 1000-mAh battery. This makes LoRaWAN particularly suited for the data collection applications (e.g., monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty-cycle restriction at the gateway. For this, we propose FREE , a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting the synchronized bulk data transmission. This means data are buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated over ten years independent of traffic type and network size. In comparison to poor scalability, low data delivery and device lifetime of fewer than two years for acknowledged data traffic in the standard LoRaWAN configurations.

SMART, a recently proposed dynamically reconfigurable NoC, enables single-cycle long-distance communication by building single-bypass paths. However, such a single-cycle single-bypass path will be broken when contention occurs. Thus, lower-priority packets will be buffered at intermediate routers with blocking latency from higher-priority packets, and extra router-stage latency to rebuild remaining path, reducing the bypassing benefits that SMART offers. In this paper, we for the first time propose an effective routing strategy to achieve nearly contention-free bypassing in SMART NoC. Specifically, we identify two different routes for communication pairs: direct route, with which data can reach the destination in a single bypass; and indirect route, with which data can reach the destination in two bypasses via an intermediate router. If a direct route is not found, we would alternatively resort to an indirect route in advance to eliminate the blocking latency, at the cost of only one router-stage latency. Compared with the current routing, our new approach can effectively isolate conflicting communication pairs, greatly balance the traffic loads and fully utilize bypass paths. Experiments show that our approach makes 22.6% performance improvement on average in terms of communication latency. Ministry of Education (MOE) Accepted version This work is partially supported by MoE AcRF Tier 2 MOE2019-T2-1-071 and Tier 1 MOE2019-T1-001-072, NTU NAP M4082282 and SUG M4082087, Singapore.

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine. Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211] The work of Sultan Zavrak was supported by the Scienti~c and Technological Research Council of Turkey (TUBITAK) through the 2211/C Ph.D. Scholarship Programme for Priority Areas. WOS:000544044400003 2-s2.0-85086985318

Smart grid communication networks adopt a variety of communication technologies interconnecting numerous and diverse equipment. The requirement of supporting a large traffic volume over such networks efficiently, reliably, and fairly among various applications calls for the study of the effectiveness of the transmission control protocol (TCP). The question raised in this paper is whether various TCP variants perform differently. We are particularly interested in comparing the performance of TCP-Reno and TCP-Vegas variants when handling sensory and metering traffic. The former variant follows a loss-based congestion control mechanism while the latter adds delay-based enhancements. Contrary to expectations, our simulation and analytical comparison results show that TCP-Reno outperforms TCP-Vegas in terms of packet loss rate. However, to take advantage of Vegas’ added features, a previously proposed scheme named split- and aggregated-TCP (SA-TCP) is recommended. The scheme splits meters’ TCP connections at intermediate devices and forwards collected data over an aggregated TCP connection to a data center.

WebRTC comprises a set of novel technologies and standards that provide Real-Time Communication on Web browsers. WebRTC makes simple the embedding of voice and video communications in all types of applications. However, releasing those applications to production is still very challenging due to the complexity of their testing. Validating a WebRTC service requires assessing many functional (e.g. signaling logic, media connectivity, etc.) and non-functional (e.g. quality of experience, interoperability, scalability, etc.) properties on large, complex, distributed and heterogeneous systems that spawn across client devices, networks and cloud infrastructures. In this article, we present a novel methodology and an associated tool for doing it at scale and in an automated way. Our strategy is based on a black-box end-to-end approach through which we use an automated containerized cloud environment for instrumenting Web browser clients, which benchmark the SUT (System Under Test), and fake clients, that load it. Through these benchmarks, we obtain, in a reliable and statistically significant way, both network-dependent QoS (Quality of Service) metrics and media-dependent QoE (Quality of Experience) indicators. These are fed, at a second stage, to a number of testing assertions that validate the appropriateness of the functional and non-functional properties of the SUT under controlled and configurable load and fail conditions. To finish, we illustrate our experiences using such tool and methodology in the context of the Kurento open source software project and conclude that they are suitable for validating large and complex WebRTC systems at scale.

In this article we introduce Kurento, an open source WebRTC media server and a set of client APIs aimed to simplify the development of applications with rich media capabilities for the Web and smartphone platforms. Kurento features include group communications, transcoding, recording, mixing, broadcasting and routing of audiovisual flows, but also provides advanced media processing capabilities such as computer vision and augmented reality. It is based on a modular architecture, which makes it possible for developers to extend and customize its native capabilities with third party media processing algorithms. Thanks to all this, Kurento can be a powerful tool for Web developers who may find natural programming with its Java and JavaScript APIs following the traditional three-tiered Web development model.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Stringent demands for a continuous evolution of cellular networks push today academic and industrial researchers to re-think backhaul solutions for 5G. In one hand, wireless backhaul solutions are cost effective and easy to deploy but suffer from limited capacity. On the other hand, wired solutions have the potential to meet bandwidth requirements but usually involve higher costs. Thus, adoption of heterogeneous technologies will be necessary. Moreover, in 5G, access and backhaul networks will work closely, and therefore, total separation of their resources may not be possible anymore; rather, cooperation between the two portions of the cellular network is desirable. Subsequently, cooperative access-backhaul mechanisms become necessary to ensure the best use of the scarce resources, i.e. bandwidth. Hence, in this paper we present the idea of spectrum sharing among different links from a cooperative access-backhaul mechanism point of view. We present simulation results for different approaches of such sharing from a common spectrum pool. The results show that traffic-aware approaches show increased fairness thus reinforcing the idea of cooperative access-backhaul mechanisms as essential strategies in current and future networks.

In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g. with mixed integer linear programming. However, determining these solutions requires solving complex optimization problems and, thus, cannot be typically done at runtime. Instead, heuristics for these problems are often created but designing them is non-trivial in many cases. The routing framework proposed here presents an alternative to the design of heuristics, whilst still achieving good performance. This is done by building a DL model trained on the optimal decisions over flows from known traffic demands. To evaluate our solution, we focused on the problem of network congestion, even though a wide range of alternative objectives could be fitted into this framework. We ran experiments using two publicly available datasets of networks with real traffic demands and showed that our solution achieves close-to-optimal network congestion values., This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001., info:eu-repo/semantics/publishedVersion

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 5G is envisioned as the key technology for guaranteeing low-latency wireless services. Packets will be marked with QoS Flow Indicators (QFI) for different forwarding treatment. 3GPP defines the end-to-end delay limits, but leaves the QoS provisioning methods as implementation dependent. Different services with different constraints will inevitably share queues at some network entity. On the one hand, maintaining the shared queues uncongested will guarantee a rapid packet delivery to the subsequent entity. A brief sojourn time is indispensable for an on time low-latency priority traffic delivery. On the other hand, if shared queues are maintained undersized, throughput will be squandered. In this paper, we propose the use of AQM techniques in 5G networks to guarantee delay limits of QoS flows. Through the evaluation of realistic delay-sensitive and background traffic, we compare different possible solutions. We show that AQM mechanisms together with limited queues, maintain the system uncongested, which reduces drastically the delay, while effectively achieving the maximum possible throughput.

The VST-TDMA access protocol has shown to have many advantages over existing protocols in centralized (star) wireless network architectures. As most data-link layer protocols, it does not have a recovery feature as data integrity is supervised by the connection-oriented transport protocols. Nevertheless, VST-TDMA possesses the flexibility to easily incorporate a retransmission feature that allows the users to experience higher throughput when using applications that use the Transmission Control Protocol, which transports approximately 90% of all Internet traffic. Results show that depending on the wireless link reliability, i.e., the frame loss rate, the throughput can be improved by a factor of ten. The VST-TDMA access protocol has shown to have many advantages over existing protocols in centralized (star) wireless network architectures. As most data-link layer protocols, it does not have a recovery feature as data integrity is supervised by the connection-oriented transport protocols. Nevertheless, VST-TDMA possesses the flexibility to easily incorporate a retransmission feature that allows the users to experience higher throughput when using applications that use the Transmission Control Protocol, which transports approximately 90% of all Internet traffic. Results show that depending on the wireless link reliability, i.e., the frame loss rate, the throughput can be improved by a factor of ten.

Surveying and Analyzing Urban Environment Approaches of Air Quality Monitoring -- A Novel Color Image Encryption Scheme Using Logistic Map and Quadratic Map Systems -- Evolutionary Multi Optimization Business Process Designs using MR-Sort NSGAII -- Design and Implementation of an Intrusion Prevention System for Wi-Fi Networks 802.11 Ac -- DoTRo: A New Dominating Tree Routing Algorithm for Efficient and Fault-Tolerant Leader Election in WSNs and IoT Networks -- An Enhancement Approach for securing Neighbor Discovery in IPv6 networks -- Dynamic Team Access Control for Collaborative Internet of Thing -- Road Anomaly Detection using Smartphone: a Brief Analysis -- Methodology for the implementation of QoS in li-fi networks in 5th generation environments -- Mobile Agent System based Cloud Computing for Ubiquitous Telemonitoring Healthcare -- Alternative Connectivity Metric for Routing in VANETs -- Num Ant Factor Based Comprehensive Investigations over Linguistic Trust and Reputations Model in Mobile Sensor Networks -- Position Certainty Propagation: A Location Service for MANETs -- Low Energy Clustering and Routing Based on Genetic Algorithm in WSNs -- IoT Service QoS Guarantee Using QBAIoT Wireless Access Method -- Recognition over Encrypted Faces -- Location Assignment of Capacitated Services in Smart Cities -- Network Slicing Architecture and Dependability -- A Hierarchical k-Anonymous Technique of Graphlet Structural Perception in Social Network Publishing -- ARP: Attack Detection Software Poisoning and Sniffers in WLAN Networks Implementing Supervised Machine Learning -- VISECO: An Annotated Security Management Framework for 5G -- Use of KRACK Attack to Obtain Sensitive Information -- Adaptive ARMA-based Prediction of Server CPU Consumption in Datacenters -- Congestion Control in a Location Service for VANETs -- Latency and Network Lifetime Trade-off in Geographic Multicast Routing for Multi-Sink Wireless Sensor Networks -- Non-Interfering Multipath Mechanism for Media Stream Transmission in Wireless Sensor Networks -- Wireless Fog-Mesh: A Communication and Computation Infrastructure for IoT based Smart Environments. .

International audience; This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

International audience; This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Mobile, Secure and Programmable Networking, held in Mohammedia, Morocco, in April 2019. The 23 papers presented in this volume were carefully reviewed and selected from 48 submissions. They discuss new trends in networking infrastructures, security, services and applications while focusing on virtualization and cloud computing for networks, network programming, software defined networks (SDN) and their security.

In this study the authors are interested in safety-critical real-time applications implemented on distributed architectures supporting the time-sensitive networking (TSN) standard. The on-going standardisation of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet standard supporting safety-critical systems and guaranteed quality-of-service. TSN will support time-triggered (TT) communication based on schedule tables, audio-video-bridging (AVB) flows with bounded end-to-end latency as well as best-effort messages. The authors first present a survey of research related to the optimisation of distributed cyber-physical systems using real-time Ethernet for communication. Then, the authors formulate two novel optimisation problems related to the scheduling and routing of TT and AVB traffic in TSN. Thus, the authors consider that they know the topology of the network as well as the set of TT and AVB flows. The authors are interested to determine the routing of both TT and AVB flows as well as the scheduling of the TT flows such that all frames are schedulable and the AVB worst-case end-to-end delay is minimised. The authors have proposed an integer linear programming formulation for the scheduling problem and a greedy randomised adaptive search procedure-based heuristic for the routing problem. The proposed approaches have been evaluated using several test cases.

The paper describes development of the methods and algorithm for predicting telematics on the vehicle’s route. De- scribed data management technology on global and local wireless networks, provides methods for managing data on the telematics environment, implemented a prototype of the data management subsystem. The time dependencies of the execution of queries are analyzed depending on the amount of data for the data warehouse on board the vehicle and for the cloud service. The result of the work is recommendations for developing connection management methods in wireless multiprotocol networks on vehicle devices. The paper describes the result of the development and research methods for predicting the telematics environment, for obtaining and analyzing the temporal characteristics of their functioning from global and local telematic maps.

Traffic demand in the access has grown in the last years, and service providers need to upgrade their infrastructure to the latest access standards. While fiber has become the preferred technology of choice in access networks, there are many fibre access technologies available in the market. This poses a challenging question to operators not always easy to answer: how to upgrade? what technology and for how long it will cope with the demands? In this paper we model the traffic forecast in the access for the next decade and analyze possible upgrade scenarios of fibre access networks, concluding which of the NG-PON flavors could better fit the demand., 5 figures, 1 table Accepted in IEEE Communications Magazine

The fifth generation (5G) of cellular systems is introducing Ultra-Reliable Low-Latency Communications (URLLC) services alongside more conventional enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular architecture is evolving from a base station-centric deployment to a fog-like set-up that accommodates a flexible functional split between cloud and edge. In this paper, a novel solution is proposed that enables the non-orthogonal coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge Nodes (ENs), while eMBB communications are handled centrally at a cloud processor as in a Cloud-Radio Access Network (C-RAN) system. This solution guarantees the low-latency requirements of the URLLC service by means of edge processing, e.g., for vehicle-to-cellular use cases, as well as the high spectral efficiency for eMBB traffic via centralized baseband processing. Both uplink and downlink are analyzed by accounting for the heterogeneous performance requirements of eMBB and URLLC traffic and by considering practical aspects such as fading, lack of channel state information for URLLC transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity, and different coexistence strategies, such as puncturing., Submitted as Journal Paper

International audience; Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous traffic. This paper presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans. With the help of the proposed example IDS, we show to what extent the training attack (and more sophisticated variants of it) has an impact on machine-learning based detection schemes, and how it can be detected.

The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated. COST Action IC1304: Autonomous Control for a Reliable Internet of Services (ACROSS)

The seven articles in this special section focus on time sensitive networking (TSN), an IEEE 802.1 standard. TSN makes it possible to carry data traffic of time-critical and/or mission- critical applications over a bridged Ethernet network shared by various kinds of applications having different Quality of Service (QoS) requirements, i.e., time and/or mission critical TSN traffic and non-TSN best effort traffic. TSN provides guaranteed data transport with bounded low latency, low delay variation, and extremely low data loss for time and/or mission critical traffic. By reserving resources for critical traffic, and applying various queuing and shaping techniques, TSN achieves zero congestion loss for critical data traffic. This, in turn, allows TSN to guarantee a worst-case end-to-end latency for critical data. TSN also provides ultra-reliability for data traffic via a data packet level reliability mechanism as well as protection against bandwidth violation, malfunctioning, malicious attacks, etc. TSN includes reliable time synchronization, a profile of IEEE 1588, which provides the basis for many other TSN functions.

In nowadays Internet, there is a strong trend to encrypt the traffic in order to protect users' privacy. This results in a hard challenge for traffic classification, as the payload in the packets cannot be accessed anymore. In this context, some techniques were proposed for traditional networks in order to classify this traffic. However, we could not find previous works addressing encrypted traffic classification considering the particularities of the Software-Defined Networking (SDN) paradigm. In this paper we present an OpenFlow-based classification system which combines techniques leveraging information in SSL/TLS certificates and DNS traffic to perform accurate flow-level classification for encrypted traffic. We make experiments with real-world traffic to evaluate the overall classification accuracy of our system as well as the accuracy detecting specific popular applications (e.g., Netflix). Furthermore, we assess the processing overhead when deploying our system in SDN environments. As a result, we observe that the support provided by OpenFlow enables to achieve a high accuracy with a more reduced processing overhead than in traditional networks, where typically the whole traffic is mirrored to an external collector that classifies the traffic.

In an always connected world, cyber-attacks and computer security breaches can produce significant financial damages as well as introduce new risks and menaces in everyday's life. As a consequence, more and more sophisticated packet screening/filtering solutions are deployed everywhere, typically on network border devices, in order to sanitize Internet traffic. Despite the obvious benefits associated to the proactive detection of security threats, these devices, by performing deep packet inspection and inline analysis, may both affect latency-sensitive traffic introducing non-negligible delays, and increase the energy demand at the network element level. Starting from these considerations, we present a selective routing and intrusion detection technique based on dynamic statistical analysis. Our technique separates latency-sensitive traffic from latency-insensitive one and adaptively organizes the intrusion detection activities over multiple nodes. This allows suppressing directly at the network ingress, when possible, all the undesired components of latency-insensitive traffic and distributing on the innermost nodes the security check for latency sensitive flows, prioritizing routing activities over security scanning ones. Our final goal is demonstrating that selective intrusion detection can result in significant energy savings without adversely affecting latency-sensitive traffic by introducing unacceptable processing delays.

We present SBAR, a monitoring system compliant with OpenFlow that provides flow-level measurement reports similar to those of NetFlow in traditional networks, but additionally enriched with labels that classify flows at the application layer. For the sake of scalability, we implement flow sampling to control both, the processing overhead in SDN controllers and the memory needed in switches to maintain the flow measurements. Moreover, we leverage the particularities of OpenFlow networks to implement a combination of classification techniques based on DPI and Machine Learning without incurring in high overheads. In particular, we accurately classify the traffic at two different levels: (i) every monitored flow is classified by application protocol, and (ii) for web and encrypted traffic, we apply specific DPI techniques to identify the applications generating each flow. In our demo, we will use real-world traffic to generate flow-level reports with SBAR that are then processed by a commercial monitoring tool to provide a comprehensive high-level view of the traffic in the network [6].

The rise of Software Defined Networking (SDN) presents an opportunity to overcome the limitations of rigid and static traditional Internet architecture and provide services like network layer multicast for live video streaming. In this paper we propose mCast, an SDN-based architecture for live streaming, to reduce the utilization of network and system resources for both Internet Service Providers (ISP) and Content Delivery Networks (CDN) by using multicast over the Internet. We propose a communication framework between ISPs and CDNs to enable mCast while retaining user and data privacy. mCast is transparent to the clients and maintains the control of CDNs on user sessions. We developed a testbed and performed large scale evaluation and comparison. Results showed that mCast can improve the video quality received by clients and, for CDNs and ISPs in comparison to IP unicast, mCast can decrease link utilization by more than 50% and network losses to 0%.

The paper focuses on the evaluation of the combined effect of Traffic Signal Control Strategy (TSC) and Variable Message Sign (VMS). With reference to the TSC a dynamic selection strategy based on macroscopic flow variables was considered for off-line traffic signal plans design. The combination of two ITS solutions, TSC and VMS, was tested through microscopic approach by SUMO traffic simulator which allows to directly reproduce the pollutant emissions and fuel consumptions.

International audience; HTTP Adaptive Streaming (HAS) is a widely used video streaming technology that suffers from a degradation of user's Quality of Experience (QoE) and network's Quality of Service (QoS) when many HAS players are sharing the same bottleneck link and competing for bandwidth. The two major factors of this degradation are: the large OFF period of HAS, which causes false bandwidth estimations, and the TCP congestion control, which is not suitable for HAS given that it does not consider the different video encoding bitrates of HAS. This paper proposes a HAS-based TCP congestion control, TcpHas, that minimizes the impact of the two aforementioned issues. It does this by using traffic shaping on the server. Simulations indicate that TcpHas improves both QoE, mainly by reducing instability, and QoS, mainly by reducing queuing delay and packet drop rate.

Aggregation of metro-flow traffic models is proposed to obtain valid core traffic predictive models for core VNT reconfiguration when metro and core networks are independently controlled. Exhaustive simulation results reveal large optical transponders usage savings.

Analyzing IP flows is an essential part of traffic measurement for cyber security. Based on information from IP flows, it is possible to discover the majority of concurrent cyber threats in highspeed, large-scale networks. Some major prevailing challenges for IP flow analysis include, but are not limited to, analysis over a large volume of IP flows, scalability issues, and detecting cyber threats in real time. In this article, we discuss the transformation of present IP flow analysis into a stream-based approach to face current challenges in IP flow analysis. We examine the possible positive and negative impacts of the transformation and present examples of real-world applications, along with our recommendations. Our ongoing results show that stream-based IP flow analysis successfully meets the above-mentioned challenges and is suitable for achieving real-time network security analysis and situational awareness.

The network functions virtualization paradigm is rapidly gaining interest among Internet service providers. However, the transition to this paradigm on ISP networks comes with a unique set of challenges: legacy equipment already in place, heterogeneous traffic from multiple clients, and very large scalability requirements. In this article we thoroughly analyze such challenges and discuss NFV design guidelines that address them efficiently. Particularly, we show that a decentralization of NFV control while maintaining global state improves scalability, offers better per-flow decisions and simplifies the implementation of virtual network functions. Building on top of such principles, we propose a partially decentralized NFV architecture enabled via an enhanced software-defined networking infrastructure. We also perform a qualitative analysis of the architecture to identify advantages and challenges. Finally, we determine the bottleneck component, based on the qualitative analysis, which we implement and benchmark in order to assess the feasibility of the architecture.

The exponential growth of mobile traffic is forcing operators to quickly increase the capacity of their networks by means of new technologies and advanced architectures. This capacity expansion not only brings increasing fixed costs for additional network infrastructures, but also inflates operational costs, which are becoming critical, mainly in terms of energy bills. In this perspective, monitoring the energy consumption of network devices and defining their energy profile models are valuable approaches for estimating energy costs and identifying the most efficient configurations. In this article, we propose an energy profiling approach that simplifies the characterization of different base station components and allows the estimation of the network energy efficiency relying only on traffic statistics. We have validated the approach over the extensive dataset of real measurements provided by a probe network for monitoring live energy consumption of Vodafone sites in three different countries.

In this paper we consider the influence of a call forwarding service on the traffic process in modern telecommunication networks. We analyse in detail only the case when the called user is busy. It is proved that call forwarding not only increases utilization of servers and the percent of successful calls but it also increases the call loss. On the simple example, we showed that this call loss increase is greater in the case of local (internal) and incoming calls, but smaller in the case of outgoing calls. The reason for such behaviour is in the role of call forwarding function in the case of internal and incoming traffic. In that situation call forwarding function decreases the effect of limited number of users on the decrease of offered traffic, comparing to the case of internal and incoming traffic without call forwarding (Engset traffic model). This statement is illustrated by comparative graphics of traffic loss without call forwarding function, and with this function when considering, separately, internal, incoming, and outgoing traffic. [Projekat Ministarstva nauke Republike Srbije, br. TR32007: Multiservice optical transport platform with OTN/40/100 Gbps DWDM/ROADM and Carrier Ethernet functionality]

Internet of Things (IoT) traffic will become increasingly heterogeneous not only in terms of traditional metrics as required bandwidth and maximum latency, but also in terms of functional requirements such as compute power and temporary storage. Sophisticated planning and engineering approaches must be adopted by service providers to account for this heterogeneity, inherent in IoT applications. Metropolitan Area Networks (MANs) are ideally suited to manage and implement resource provisioning of heterogeneous IoT application traffic and, as a result, possess a unique ability to conserve MAN and Wide Area Network (WAN) bandwidth costs. We propose a novel comprehensive MAN resource provisioning model in a hybrid fog-cloud architecture which decouples compute and storage functions while accounting for traffic of a set of heterogeneous parameterized application profiles. This is intended to assist the MAN service provider to minimize the total operational cost of provisioning IoT traffic demands as well as provide a framework for dynamic lightpath reallocation within the MAN. The model demonstrates which application profile and topological parameters have the most significant effect on the individual cost components. As a result of the model, we demonstrate that optimal resource provisioning, i.e. whether functions are placed in the fog or cloud, depends heavily on application computational complexity, compression factor, and latency budget, as well as proportions of local and global traffic.

Cloud Radio Access Network (C-RAN) has attracted a worldwide attention in both academia and industry. This network architecture re-forming has been considered as a potential solution to meet the increasing capacity demands for future mobile data traffic. In addition, Network Virtualisation is a promising technique for efficient resource utilisation. This paper proposes a customisable resource virtualisation algorithm for multi user data scheduling in a Long Term Evolution (LTE) C- RAN deployment. The algorithm is based on the hypervisor specific assignment of air resources allocation between the virtual operators (VOs) dynamically, based on either joint scheduling or per cell schemes. The objective is to improve the resource allocation mechanism based on traffic conditions and a database of pre-defined services priorities. Two distinctive scenarios are considered and evaluated against standard Round Robin (RR) C-RAN scheduling technique. Simulation results show improvements in the overall traffic throughput and reduction in end-to-end delay for delay sensitive applications. In addition, an assessment of fairness guarantee is considered across all users.

In a smart power grid, collecting data from a large number of smart meters and sensors over the conventional one-hop transmission control protocol (TCP) communication is prone to a high packet loss rate and degraded throughput due to the ineffectiveness of the TCP congestion control mechanism. The Split and Aggregated TCP (SA-TCP) proposes upgrading intermediate devices (known as regional collectors or RCs) to combine meters' TCP connections and forward data over a unified TCP connection to the utility server. This paper provides a comprehensive performance analysis of the SA-TCP scheme. It studies the impact of varying various parameters on the scheme, including the impact of network link capacity, buffering capacity of RCs, propagation delay between the meters and the utility server, and finally the number of RCs utilized as SA-TCP aggregators. The performance results show that by adjusting those parameters, it is possible to tune throughput of a smart metering network to the desired amount while keeping the deployment cost minimal. 2014 Elsevier Ltd. This work was made possible by NPRP 6 - 711 - 2 - 295 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

A substantial amount of work has recently gone into localizing BitTorrent traffic within an ISP in order to avoid excessive and often times unnecessary transit costs. Several architectures and systems have been proposed and the initial results from specific ISPs and a few torrents have been encouraging. In this work we attempt to deepen and scale our understanding of locality and its potential. Looking at specific ISPs, we consider tens of thousands of concurrent torrents, and thus capture ISP-wide implications that cannot be appreciated by looking at only a handful of torrents. Second, we go beyond individual case studies and present results for few thousands ISPs represented in our data set of up to 40K torrents involving more than 3.9M concurrent peers and more than 20M in the course of a day spread in 11K ASes. Finally, we develop scalable methodologies that allow us to process this huge data set and derive accurate traffic matrices of torrents. Using the previous methods we obtain the following main findings: i) Although there are a large number of very small ISPs without enough resources for localizing traffic, by analyzing the 100 largest ISPs we show that Locality policies are expected to significantly reduce the transit traffic with respect to the default random overlay construction method in these ISPs; ii) contrary to the popular belief, increasing the access speed of the clients of an ISP does not necessarily help to localize more traffic; iii) by studying several real ISPs, we have shown that soft speed-aware locality policies guarantee win-win situations for ISPs and end users. Furthermore, the maximum transit traffic savings that an ISP can achieve without limiting the number of inter-ISP overlay links is bounded by “unlocalizable” torrents with few local clients. The application of restrictions in the number of inter-ISP links leads to a higher transit traffic reduction but the QoS of clients downloading “unlocalizable” torrents would be severely harmed. The research leading to these results has been partially funded by the European Union's FP7 Program under the projects eCOUSIN (318398) and TREND (257740), the Spanish Ministry of Economy and Competitiveness under the eeCONTENT project (TEC2011-29688-C02-02), and the Regional Government of Madrid under the MEDIANET Project (S2009/TIC-1468). Publicado

Spectrum sharing is becoming a necessity in future cellular networks due to the increasing traffic demand and challenges of getting exclusive spectrum. This article reviews the spectrum sharing framework that consists of regulatory, technology, and business domains. For future mobile network operators, sharing of spectrum with other operators or with other radiocommunication services - especially when using cognitive radio system technologies - is a disruptive change. Building on alternative spectrum sharing scenarios, this article discusses a set of Simple Rules for mobile network operators, both dominators and challengers, regarding spectrum sharing in future cognitive cellular networks. The Simple Rules provide a dynamic framework for both dominating and challenger mobile network operators for developing their sharing-based business models.

We report results from the recently finished QUASAR project, which has studied overall system aspects of cognitive radio technologies and has paid attention particularly to the economic viability of different use cases. We find that successful secondary sharing goes far beyond the detection of spectrum holes. Large-scale commercial success requires that secondary systems are scalable so that a large number of users can be served in an economically viable fashion. Our key finding is that secondary spectrum use is not an attractive method for most of the commercially interesting scenarios, from neither a business nor technical perspective. Perhaps somewhat surprisingly, the likely commercial "sweet spot" for secondary sharing in the lower frequency bands is short-range indoor communications. We also find that regulation does not currently present a significant barrier in Europe or the United States. QC 20150626 QUASAR

6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2016; Malta; Malta; 13 November 2016 through 17 November 2016, The basic problem of whether direct transmission or multi- hop routing increases goodput in multi-hop wireless net- works still lacks investigation from many aspects. This ar- ticle, approaches this problem by considering the effect of different traffic arrival distributions on the choice of rout- ing strategy for enhancing goodput of IEEE 802.11 DCF based multi-hop wireless networks under hidden terminal existence. Different traffic arrival distributions including Poisson, constant bit rate (CBR), Pareto and Exponential are considered, relaxing the generally adopted Poisson as- sumption, for various data rates over a wide range of traffic loads extending from unsaturated to saturated traffic loads. The goodput performance for all traffic arrival distribu- tions is found to be dependent on the traffic load in multi- hop networks. Of the four traffic models used, the network achieved the best goodput with Pareto and Exponential ar- rival distributions for light traffic loads, where CBR per- forms slightly better under heavy loads. The results suggest that a traffic load-aware pre-control mechanism on the traffic arrivals to the IEEE 802.11 MAC layer might provide signicant goodput gains in multi-hop wireless networks.

The concepts of wisdom of crowd and collective intelligence have been utilized by mobile application developers to achieve large-scale distributed computation, known as crowd computing. The profitability of this method heavily depends on users' social interactions and their willingness to share resources. Thus, different crowd computing applications need to adopt mechanisms that motivate peers to collaborate and defray the costs of participating ones who share their resources. In this article, we propose OPENRP, a novel, lightweight, and scalable system middleware that provides a unified interface to crowd computing and opportunistic networking applications. When an application wants to perform a device-to-device task, it delegates the task to the middleware, which takes care of choosing the best peers with whom to collaborate and sending the task to these peers. OPENRP evaluates and updates the reputation of participating peers based on their mutual opportunistic interactions. To show the benefits of the middleware, we simulated the behavior of two representative crowdsourcing applications: message forwarding and task offloading. Through extensive simulations on real human mobility traces, we show that the traffic generated by the applications is lower compared to two benchmark strategies. As a consequence, we show that when using our middleware, the energy consumed by the nodes is reduced. Finally, we show that when dividing the nodes into selfish and altruistic, the reputation scores of the altruistic peers increase with time, while those of the selfish ones decrease.

ABNO's OAM Handler is extended with big data analytics capabilities to anticipate traffic changes in volume and direction. Predicted traffic is used to trigger virtual network topology re-optimization. When the virtual topology needs to be reconfigured, predicted and current traffic matrices are used to find the optimal topology. A heuristic algorithm to adapt current virtual topology to meet both actual demands and expected traffic matrix is proposed. Experimental assessment is carried out on UPC's SYNERGY testbed.

With the incremental amount of applications running over the telecom cloud architecture it is becoming of paramount importance being able to run simulations aiming at evaluating the performance of such applications. To that end, one of the key elements in the simulation is how to generate network traffic. In this paper we propose realistic traffic functions that can be used for such purposes and present how those functions have been integrated in our OMNET++-based simulator.