Search

With the growth in the use of smart mobile devices and the development of information technologies, worms and malware can spread from mobile networks into heterogeneous and hierarchical networks. Thus, the spread of these worms constitutes an increasing potential threat. For understanding the propagation of the aforementioned wireless hybrid worms, current researches have three critical problems: Structural simplification of network topologies (previous research object for wireless worms is the mobile network), Homogenous population of network devices (properties of network devices are the same), and Inaccuracy of propagation models (traditional deterministic differential or stochastic difference models cannot model propagation of wireless hybrid worms accurately). To address them, we propose a novel compartmental population-based propagation model oriented towards heterogeneous and hierarchical networks with human behaviors, and then study the impacts of user mobility and operation behaviors on worm propagation. Meanwhile, we conduct extensive simulations to show our model can characterize propagation features accurately. The results in this paper not only provide a deep understanding of new worm propagation, but also serve as fundamental defense guidelines.

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Thing (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide Data-As-A-Service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this paper, we study the Task Type-based Computation Offloading algorithm (TTCO) to implement such platform. We use the "IoT-Edge-Cloud" three-layer multi-hop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash Equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

Distributed denial of service (DDoS) attacks have always been a nightmare for network infrastructure for the last two decades. Existing network infrastructure is lacking in identifying and mitigating the attack due to its inflexible nature. Currently, software-defined networking (SDN) is more popular due to its ability to monitor and dynamically configure network devices based on the global view of the network. In SDN, the control layer is accountable for forming all decisions in the network and data plane for just forwarding the message packets. The unique property of SDN has brought a lot of excitement to network security researchers for preventing DDoS attacks. In this article, for the identification of DDoS attacks in the OpenFlow controller, a probabilistic technique with a central limit theorem has been utilized. This method primarily detects resource depletion attacks, for which the DARPA dataset is used to train the probabilistic model. In different attack scenarios, the probabilistic approach outperforms the entropy-based method in terms of false negative rate (FNR). The emulation results demonstrate the efficacy of the approach, by reducing the FNR by 98% compared to 78% in the existing entropy mechanism, at 50% attack rate.

Previous theoretical studies have identified possible conditions for an undetectable false data injection attack (FDIA) vector that bypasses bad data detectors in power system state estimators (SE). The formulation of the FDIA vector has attracted many researchers to propose detection algorithms. However, neither the attack formulation nor the proposed detection algorithms have been experimentally proven or validated. In this paper, a framework for developing cybersecurity testbeds that evaluate FDIA vectors for utility-scale distributed energy resources (DERs) and SE in real-time is proposed and implemented. The testbed is a combination of: 1) simulation models of a utility-scale DER and distribution systems using OPAL-RT; 2) intelligent electronic devices (SEL-351S, SEL-311C and SEL-3355); 3) network devices; and 4) graphical user interface (LabVIEW). The combination of actual devices, modeled systems, and cyber-physical interfaces adds realistic constraints such as sampling error, network jitter, and data time alignment that exist during actual power system operations. Using the testbed framework, results show that the onset of the FDIA against a SE is experimentally detectable. Next, the testbed is used to expose an unknown vulnerability of existing phase locked loop (PLL) techniques by presenting a new class of attack that causes PLL reporting of false phase angle and frequency values to the DER controllers. This type of attack would then induce the DERs to perform an adverse control action. In this research, a new detection algorithm based on a Kalman filter is evaluated to detect this type of PLL attack. A detailed evaluation of various attack scenarios is presented. The results indicate the benefits of employing this type of testbed framework to improve the development of cybersecurity defenses for realistic inverter based DER environments.

Despite the advantages of multi-access edge computing in enabling latency-sensitive services and extending the limited computing capabilities of network devices, access communication issues are still often causing the quality of the wireless channels to be severely degraded, preventing the edge resources from being efficiently utilized. Through the deployment of low-cost passive reflecting elements, the recent studies of intelligent reflecting surfaces (IRSs) in wireless networks have shown a great potential for enhancing the quality of the wireless channels and the transmission rates. In this work, motivated by the recent findings, we study the use of an IRS-aided edge computing system for enabling low latency and high reliability computation offloading in the context of a single-user network. Specifically, we optimize the phase shift of the IRS elements along with the device’s transmit power and offloading decision, with the objective of minimizing the device’s energy consumption. Due to the non-convexity of the problem, we propose a customized sub-optimal solution based on the alternating optimization approach, utilizing novel successive convex approximation techniques. Numerical analysis demonstrates the energy reduction and saving in network resources provided by the optimized use of the IRS, especially for offloading services with higher reliability.

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimediarich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks.\ud \ud Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches.

In the 6G network era, energy-efficient computing means higher resource utilization and lower energy consumption. The traditional computing model regards the network as a transmission pipeline and has not fully explored the potential of network devices. In-network computing is a new type of computing model that delegates application-layer processing functions to the network data plane. The traffic can be processed during transmission, and the amount of traffic is reduced. As a result, the computing pressure and energy consumption of the cloud-side system is reduced. Most of the existing in-network computing models are based on programable network devices. Due to the limitations of the TCP/IP protocol, these methods can only handle application messages at the packet level. We propose an energy-efficient in-network computing paradigm for 6G that integrates network functions into a general computing platform instead of delegating computing tasks to network devices. The computing platform that integrates network functions replaces traditional network devices and acts as a network node. Unlike traditional network devices, the network node provides a unified operating environment for application tasks through hypervisors and containers. Therefore, data processing tasks can be deployed on cloud servers or on network nodes. The network controller schedules the processing tasks to execute on the network node passing the application traffic. The experimental results show that our paradigm can make full use of the computing resources of network nodes and significantly reduce the computing pressure, network transmission overhead and energy consumption of the data centre.

Forecasting the users’ movements and behaviors is extremely valuable for early warning systems to provide high-quality service in wireless and cellular networks. However, forecasting the service of the specific network devices with the additional knowledge of user behaviors is underexplored. This study proposes a WiFi6-specific QoS forecasting engine, which uses a spatio-temporal graph approach to predict QoS parameters, e.g., throughput, in terms of user position in WiFi6 networks. Since WiFi6 networks are planning to meet various traffic types with dense users, it is crucial to analyze it in both spatial and temporal manner by preserving graph-structured data. In this study, we modeled the problem with a novel deep learning approach, Graph Convolution Networks (GCNs), by adapting the Omni-Scale 1D CNN for temporal analysis. Then, we analyze the forecasting performance with two datasets in terms of variety error metrics over loss rate, link speed, throughput, and round trip time (RTT). Also, we give the baselines, ARIMA, FARIMA, SVR, and RNN to compare the proposed solution in terms of accuracy. Finally, we present the simulation results to compare the proposed QoS forecasting approach with user mobility forecasting. All experiments show that proposed WiFi6-specific QoS forecasting gives superior results for multi-horizon QoS prediction with respect to user positions considering heterogeneous traffic types.

In this article, we revisit the problem of how to provide "native" IP multicast support to enterprise distributed applications in today's clouds, without touching either the application implementation or the underlying network hardware. We propose SDM, Software-Defined IP Multicast, to explore the idea of performing Multicast-to-Unicast (M2U) and Unicast-to-Multicast (U2M) translations at the virtualized network edge. At the data plane, with novel architecture designs, SDM achieves efficient M2U and U2M translations by only using Open vSwitch (OVS), an OpenFlow-compatible open-source software switch widely deployed in modern clouds. At the control plane, SDM dynamically adopts multicast trees respecting the join and leave of receivers with flexible cost-based algorithms. SDM is very easy to deploy and use, as its required features are already supported by off-the-shelf OVS software (version ≥ 2.3) and the OpenFlow protocol (version ≥ 1.1). Performance evaluations of its control-plane algorithms also imply that SDM is flexible to construct throughput and latency/height optimized multicast trees respecting task requirements.

Modern network devices and systems are raising higher requirements on network testers that are regularly used to evaluate performance and assess correctness. These requirements include high scale, high accuracy, flexibility and low cost, which existing testers cannot fulfill at the same time. In this paper, we propose HyperTester, a network tester leveraging new-generation programmable switches and achieving all of the above goals simultaneously. Programmable switches are born with features like high throughput and linerate, deterministic processing pipelines and nanosecond-level hardware timestamps, the P4 programming model as well as comparable pricing with commodity servers, but they come with limited programmability and memory resources. HyperTester uses template-based packet generation to overcome the limitations of the switch ASIC in programmability and designs a stateless connection mechanism as well as counter-based state compression algorithms to overcome the memory resource constraints in the data plane. We have implemented HyperTester on Tofino, and the evaluations on the hardware testbed show that HyperTester supports high-scale packet generation (more than 1.6Tbps) and achieves highly accurate rate control and timestamping. We demonstrate that programmable switches can be potential and attractive targets for realizing network testers.

Flexible Ethernet (FlexE) over WDM transport is one of the key technologies for achieving high-speed next generation radio access network (NG-RAN). The security issue, such as optical fiber wiretap, becomes increasingly prominent. However, optical or electrical layer secure strategies cannot support higher rate and lower overhead transport, respectively. To this end, in this paper, we propose a cross-layer secure transport strategy in end-to-end FlexE over WDM networks, which leverages Universal Hashing based FlexE data block permutation and multiple parallel fiber transmission to achieve anti-eavesdropping. Different levels of attack ability are considered for measuring the impact on network security and resources utilization. Furthermore, the trade-off between efficient resources utilization and guarantee of higher level of transport security is also explored through building an integer linear programming (ILP) model. For the computation complexity of ILP model at large scale input conditions, a Security and resource-Efficiency-oriented Ant Colony optimization algorithm (SEAC) is proposed. Simulation results show that the cross-layer defense strategy is effective to struggle against intruders with different levels of attack ability, and the SEAC algorithm can achieve near-optimal performance and outperform the traditional first-fit algorithm.

To accommodate ever-growing mobile data and heterogeneous 5G services, considerable challenges have emerged to stimulate new design principles on radio access networks (RAN). Advanced functional splits are then introduced to divide baseband unit (BBU) into two new entities, i.e., distributed unit (DU) and central unit (CU), that DU-CU can be co-located or separated into different processing pools (PP) honoring service requirements. Then, the optical transport network evolves towards a more flexible architecture to support agile DU-CU deployment (i.e., alterable DU-CU locations). However, this agile DU-CU deployment without an effective management strategy may lead to the activation of many unnecessary PPs and lightpaths in the network infrastructure that results in low energy efficiency. Moreover, this problem will be continuously amplified with network scale expansion, which becomes one of the operator's major concerns for OPEX reduction. Thus, in this paper, we are dedicated to solving this problem through turning network devices from “always on” to “on-demand on” pattern to support energy-efficient DU-CU deployment and lightpath provisioning. To this end, we propose a mixed-integer linear programming (MILP) model and auxiliary graph-based heuristic to minimize the power consumption of reconfigurable add/drop multiplexer, Ethernet switch, optical transponder, PP, and DU-CU processor. We analyze the power consumption under different 5G services (i.e., eMBB, URLLC, and mMTC) from a latency perspective in both small-scale and large-scale networks. Moreover, our strategies can also be applied with adjustable deployment that adapts to time-varying user traffics, which reduces power by over 14% compared to conventional static deployment. Our analyses can help provide insights into the modeling and designing of energy-efficient DU-CU deployment and lightpath provisioning in 5G.

Practical needs related to infrastructure management are driving optical network operators to include Raman amplification in order to improve the performance of long fiber spans. Compared to standard erbium-doped fiber amplifier (EDFA) management, Raman amplifiers require a greater degree of control and monitoring due to their distributed nature. Inevitably, this update leads to a key consideration; the introduction of additional telemetry devices with respect to standard EDFA photodiodes, resulting in an increase in required investments. In this work, we present an embedded controller architecture in combination with an ad-hoc probing procedure to manage Raman amplification within disaggregated optical networks, using only standard integrated equipment, allowing an efficient implementation without the introduction of optical channel monitors (OCMs). This proposal is validated using a fully representative experimental campaign, testing both the probing procedure on a single fiber span and the operation of a Raman amplifier using the extracted information.

Network programmers can currently deploy an arbitrary set of protocols in forwarding devices through data plane programming languages such as P4. However, as any other type of software, P4 programs are subject to bugs and misconfigurations. Network verification tools have been proposed as a means of ensuring that the network behaves as expected, but these tools frequently face severe scalability issues. In this paper, we argue for a novel approach to this problem. Rather than statically inspecting a network configuration looking for bugs, we propose to enforce networking properties at runtime. To this end, we developed P4box , a system for deploying runtime monitors in programmable data planes. P4box allows programmers to easily express a broad range of properties (both program-specific and network-wide). Moreover, we provide an automated framework based on assertions and symbolic execution for ensuring monitor correctness. Our experiments on a SmartNIC show that P4box monitors represent a small overhead to network devices in terms of latency, throughput and power consumption.

An experiment to search for mu-e conversion named COMET is being constructed at J-PARC. The experiment will be carried out using a two-stage approach of Phase-I and Phase-II. The data taking system of Phase-I is developed based on common network technology. The data taking system consists of two kinds of networks. One is a front-end network. Its network bundles around twenty front-end devices that have a 1-Gb optical network port. And a front-end computer accepts data from the devices via its network. The other is a back-end network that collects all event fragments from the front-end computers using a 10-Gb network. We used a low price 1Gb/10Gb optical network switch for the front-end network. And direct connection between the front-end PC and an event building PC using 10-Gb optical network devices was used for the back-end network. The event building PC has ten 10-Gb network ports. And each network port of the event building PC is connected to the front-end PC's port without using a network switch. We evaluated data taking performance with an event building on these two kinds of networks. The event building throughput of the front-end network achieved 337 MiB/s. And the event building throughput of the back-end networks achieved 1.2 GiB/s. It means that we could reduce the construction cost of the data taking network using this structure without deteriorating performance. Moreover, we evaluated the writing speed of the local storage RAID disk system connected to a back-end PC by a SAS interface, and a long-distance network copy from the experiment location to the lasting storage., Comment: 7 Pages, 12 figures, 22nd Virtual IEEE Real Time Conference

Virtual network embedding (VNE) in network virtualization over flexi-grid networks is one of the main challenges up to now. Many models and algorithms based on reducing the cost or keeping integrity and continuity of spectrum slots have been already proposed. In this paper, we employ the concept of distribution equilibrium of computation resources to improve performance of VNE. An integer linear programming (ILP) model and an algorithm based on the concept of distribution equilibrium for virtual node embedding are developed. The experimental results show that this algorithm has some significant advantages. And then, an ILP model based on resource distribution equilibrium and adaptive modulation for VNE over flexi-grid optical networks is established. Since the ILP problem is NP-complete, a heuristic algorithm to manipulate VNE on table is developed. The simulation results reveal the superiority in terms of acceptance ratios and system revenues.

In this paper, we propose a versatile network architecture, named VFTree, for data centers. VFTree provisions unique versatility in the sense that its topology and performance can be tailored with great freedom. The wide dynamic range and fine granularity of its configurations allow this architecture to closely follow the demands of the running traffic. Also, this versatility facilitates the reuse of network devices, so that upgrades could be achieved by rearranging existing devices, avoiding the expenditure on new generations of hardware in each upgrade cycle. Furthermore, this versatility allows VFTree to be elaborately tuned into cost efficient zones. This way, compared to widely adopted industrial solutions including fat-tree, VFTree can provide even higher aggregate bandwidth and richer path availability at the same cost. In this paper, we first design the network topology of VFTree. We parameterize the topology so that it is flexible and generalized. For example, we can configure the numbers of upward and downward ports for the switches, which are fixed in the classic fat-tree. Based on that, we propose routing algorithms, implement the abstraction from packet level to the flow level, and handle the flow collisions. We also analyze VFTree’s performance, demonstrate its aforementioned features, and compare it with its predecessors. VFTree can be seen as a generalization of, and is backward-compatible to fat-tree. We believe it is ready to be deployed in industry, as a perfect replacement of the fat-tree DCNs.

The past several decades have witnessed wide deployment of space information networks (SINs). More than 8000 satellites have been launched into Earth's orbit recently. Meanwhile, the space communications requirements have grown by a factor of 10 in the last decade. The sharply increasing traffic volume puts tremendous pressure on the network traffic control in SINs. Current end-host-based schemes have to collect a massive amount of data (e.g., explicit congestion notification) to respond to one network event (e.g., topology change, network congestion), which is unacceptable in SINs due to the large end-to-end delay. Recently, with the emergence of programmable network devices, it has become possible to perform flexible control strategies as the traffic is flowing through the network (i.e., in-network traffic control). By implementing the control directly inside the network, the in-network schemes are more effective at scale and more responsive to network dynamics. Therefore, in this article, we design an in-network traffic control powered SIN architecture to enhance the network performance. In addition, we present two use cases, in-network load balance and in-network congestion control, to demonstrate the feasibility of our architecture. Extensive simulations are performed to evaluate our proposed schemes.

In the era of the fifth-generation fixed network (F5G), optical networks must be developed to support large bandwidth, low latency, high reliability, and intelligent management. Studies have shown that software-defined optical networks (SDON) and artificial intelligence can help improve the performance and management capabilities of optical networks. Inside a large-scale optical network, many types of alarms are reported that indicate network anomalies. Relationships between the alarms are complicated, making it difficult to accurately locate the source of the fault(s). In this work, we propose a knowledge-guided fault localization method, using network alarm knowledge to analyze network abnormalities. Our method introduces knowledge graphs (KGs) into the alarm analysis process. We also propose a reasoning model based on graph neural network (GNN), to perform relational reasoning on alarm KGs and locate the network faults. We develop an ONOS-based SDON platform for experimental verification, which includes a set of processes for the construction and application of alarm KGs. The experimental results show the proposed method has high accuracy and provide motivation for the industry-scale use of KGs for alarm analysis and fault localization.

The vehicular networks constructed by interconnected vehicles and transportation infrastructure are vulnerable to cyber-intrusions due to the expanded use of software and the introduction of wireless interfaces. Intrusion detection systems (IDSs) can be customized efficiently in response to this increased attack surface. There has been significant progress in detecting malicious attack traffic using machine learning approaches. However, existing IDSs require network devices with powerful computing capabilities to continuously train and update complex network models, which reduces the efficiency and defense capability of intrusion detection systems due to limited resources and untimely model updates. This work proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices (e.g., connected vehicles and roadside units (RSUs). Distributed federated-based approach reduces resource utilization of the central server while assuring security and privacy. To ensure the security of the aggregation model, blockchain is used for the storage and sharing of the training models. This work analyzes common attacks and shows that the proposed scheme achieves cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level , Space Level and Model Level . For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.

Intrusion detection is an important technique that can provide solid protection for the network equipment against the security attacks. However, the attacks are usually unbalanced in different types and the attacks of unknown classes may also occur with the growth of Internet construction. In this case, the traditional machine learning-based intrusion detection methods usually have inferior detection accuracy and high false-positive rates. To tackle this problem, in this article, we propose a novel deep learning-based intrusion detection method named log-cosh conditional variational autoencoder (LCVAE). It inherits the capability of the conditional variational autoencoder (CVAE) that can capture the complex distribution of observed data and generate new data with prespecified classes. Different from the traditional CVAE, to better model the discrete property in the intrusion data, we design an effective loss term using the log hyperbolic cosine (log-cosh) function in the proposed LCVAE method. It can well balance the generation and reconstruction procedures and is more effective to generate diverse intrusion data for the imbalanced classes. To improve the detection accuracy, we utilize the classification based on convolutional neural network to perform feature extraction and classification based on the observed and generated intrusion data. We conduct extensive experiments on the challenging data set NSL-KDD with large-scale intrusion data. The results show that the superior detection performance of the proposed LCVAE method comparing with several state-of-the-art intrusion detection methods, and also demonstrate the potentiality of generating new intrusion data with promising diversity.

Routing in multi-layer multi-domain (MLMD) networks is challenging due to different technologies and cooperation between different layers and domains. The MLMD routing problem has been considered in prior work, however most of them paid no attention to the inter-layer (or boundary) links, and the inter-domain routing is not yet optimized due to the lack of visibility over the intra-domain network topology. In this article, we investigate the problem of orchestrating MLMD networks by a hierarchical path computation engine (PCE) to leverage the performance of FlexE—the new Flexible Ethernet technology used to link IP and optical domains. We model the routing and FlexE assignment problem for FlexE-Aware and FlexE-Unaware modes and derive an efficient algorithm that optimizes the network utilization by a hierarchical allocation of bandwidth. Facing the issue of missing network information in an MLMD network due to privacy and security reasons, our orchestrator uses a new implicit routing strategy for gathering intra-domain information where the boundary link metrics are considered. Experimental results show that the proposed solution achieves 87% of the optimal throughput, a performance significantly higher than the current practices.

Billions of IoT devices and smart objects are already in operation today and even more are expected to be on the network over time. These IoT devices will generate enormous amounts of data that cannot be allowed to transmit on the network without end-to-end encryption or any trust and security mechanism. Currently, we have certificate authorities that certify the identity of a network device by binding its identity with its public key. However, these certificate authorities are centralized in structure and will not be able to individually certify billions of IoT devices entirely. In this paper, we propose that in an SDN-based IoT network, the identities, i.e., public keys and trust indices of IoT devices, can be stored on a blockchain to ensure immutability and tamper-resistance. The paper presents a novel scalable solution for key and trust management of IoT devices in IoT networks, with a successful proof-of-concept that proves the scalability of the proposed solution. The combination of an IoT network along with blockchain technology and software-defined networking (SDN) is effectively demonstrated through simulation that is able to store the public keys of IoT devices on the blockchain and route the network traffic efficiently through SDN. The performance of the proposed solution is evaluated in terms of throughput and access time delay. The results illustrate that access delay and throughput were not affected linearly or exponentially and the proposed solution shows no significant degradation in the performance with the increase in the number of IoT nodes and packets.

Software-Defined Vehicular Networks (SDVNs) have been a vital component to various radio access technologies that supports massive data loads in various security and infotainment applications. SDVNs elevate the constraints of static hardware network devices to programmable units and provide a global view of the network's status and a common interface between varied radio access technologies. However, having a logically centralized control unit comes with several challenges, including bottleneck issues and densification issues. The distributed control plane appears as a possible alternative to the centralized one yet raises questions concerning where to deploy control units, how many SDN controllers are needed in a given network structure, and when to migrate switches or change controllers. Therefore, the development of an efficient topology management entity is an important aspect that needs to be addressed. In this article, we define the main components necessary to develop and design an efficient SDVN topology management architecture. We evaluate the current solutions and methods of controllers' deployment and assignment for software-defined vehicle networks. We then discuss several challenges, design guidelines, open issues, and future directions for research related to the development of future adaptive software-defined vehicular networks.

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models ). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline.

Recent technology evolution allows network equipment to continuously stream a wealth of “telemetry” information, which pertains to multiple protocols and layers of the stack, at a very fine spatial-grain and high-frequency. This deluge of telemetry data clearly offers new opportunities for network control and troubleshooting, but also poses a serious challenge for what concerns its real-time processing. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed up to 3Terabit/s worth of real application traffic. We contrast DenStream with offline algorithms such as DBScan and Local Outlier Factor (LOF), as well as online algorithms such as the windowed version of DBScan, ExactSTORM, Continuous Outlier Detection (COD) and Robust Random Cut Forest (RRCF). Our experimental campaign compares these seven algorithms under both accuracy and computational complexity viewpoints: results testify that DenStream (i) achieves detection results on par with RRCF, the best performing algorithm and (ii) is significantly faster than other approaches, notably over two orders of magnitude faster than RRCF. In spirit with the recent trend toward reproducibility of results, we make our code available as open source to the scientific community.

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore exhibiting tardiness and clumsiness in responding to network dynamics. Recently, with the advancement of programmable network hardware, it is possible to implement network functions inside the network. However, current in-network schemes are largely dependent on the manual process, which presents poor scalability and robustness. Therefore, in this article, we present a new intelligent network control architecture, in-network intelligence control. We design intelligent in-network devices that can automatically adapt to network dynamics by leveraging powerful machine learning adaptive abilities. In addition, to enhance the collaboration among distributed in-network devices, a centralized management plane is introduced to ease the training process of distributed switches. To demonstrate the technical feasibility and performance advantage of our architecture, we present three use cases: in-network load balance, in-network congestion control, and in-network DDoS detection.

Recently, smart cities, healthcare system, and smart vehicles have raised challenges on the capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for the devices in hotspots area. Multiaccess edge computing (MEC) can enhance the ability of emerging resource-intensive IoT applications and has attracted much attention. However, due to the time-varying network environments, as well as the heterogeneous resources of network devices, it is hard to achieve stable, reliable, and real-time interactions between edge devices and their serving edge servers, especially in the 5G ultradense network (UDN) scenarios. Ultradense edge computing (UDEC) has the potential to fill this gap, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: 1) efficient utilization of multiple 5G resources (e.g., computation, communication, storage, and service resources); 2) low overhead offloading decision making and resource allocation strategies; and 3) privacy and security protection schemes. Thus, we first propose an intelligent UDEC (I-UDEC) framework, which integrates blockchain and artificial intelligence (AI) into 5G UDEC networks. Then, in order to achieve real-time and low overhead computation offloading decisions and resource allocation strategies, we design a novel two-timescale deep reinforcement learning (2Ts-DRL) approach, consisting of a fast-timescale and a slow-timescale learning process, respectively. The primary objective is to minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation, and service caching placement. We also leverage federated learning (FL) to train the 2Ts-DRL model in a distributed manner, aiming to protect the edge devices’ data privacy. Simulation results corroborate the effectiveness of both the 2Ts-DRL and FL in the I-UDEC framework and prove that our proposed algorithm can reduce task execution time up to 31.87%.

This article proposes a novel architecture of a time division multiplexing passive optical network (TDM-PON) system with general-purpose hardware. The system operates with the point-to-point (P2P) Ethernet protocol and software processing. The architecture is redesigned from the ground up, differing from conventional PON system architectures. In recent years, researchers have attempted to softwarize a number of PON functions such as media access control (MAC) and physical functions, which run on a general-purpose central processing unit (CPU). On the other hand, these existing systems still required PON-dedicated or programmable hardware for processing the PON specific protocol. This paper proposes a TDM-PON system that requires only general-purpose hardware; a network interface card with the use of the P2P Ethernet protocol and a general-purpose server with the use of software processing. To operate the proposed system, we newly developed burst-mode operation and multipoint MAC control schemes, while taking Link Fault Signaling and CPU processing jitter into account. We quantitatively evaluated the burst-mode transmission with P2P generic Ethernet TRx as a preliminary experiment, which showed that Ton and Toff were 75 μs and less than 1 μs, respectively. Then, we measured our developed prototype system's performance for verifying the feasibility of our proposal. In specific, it achieved a 5.2 Gbps PON system consisting of an optical line terminal (OLT) server and two optical network unit (ONU) servers under the condition of a 500-ms grant cycle including a 100-ms discovery window.

The next decade will usher in a new era of technological innovation where compute, communications, and intelligence will converge. The number of connected devices is estimated to reach 500 billion by 2030, which is 59× larger than the expected world population [1] . Instead of humans dominating the use of wireless networks, objects or things will become the dominant users. Networks will incorporate intelligent compute to enable new types of applications such as cyberphysical worlds that replicate reality. These applications will require distributed intelligent compute platforms, as processing terabytes of data at a single edge device will be near impossible. Future 6G systems are currently being defined to meet these needs, as shown in Figure 1 , and the essential components of all 6G systems will be intelligence and security [2] . These new wireless systems will need to leverage a distributed compute network that is part of the wireless network so that the edge device can employ a high-performance compute platform that is in close proximity to itself. Such a network device must incorporate flexibility and programmability at low latency while also supporting network and connectivity functions. Heterogeneous integration in 2.5D and 3D can efficiently enhance existing compute platforms such as processors, application-specific integrated circuits, and field programmable gate arrays (FPGAs) to address emerging applications such as intelligent compute-communicate systems at lower cost with faster time to market. This article presents an overview of 2.5D and 3D heterogeneous technologies and how they can be leveraged to create new intelligent-compute-communicate platforms to enable emerging applications

In the last decade, many ransomware attacks had the ability to spread within local networks or even outside them. At the same time, software defined networking (SDN) has provided a major boost to networks by transferring intelligence from network devices to a programmable logically centralised controller. The latter can be programmed to be compatible with the requirements of a wide range of networks and environments in a straightforward manner. This has motivated researchers to design SDN-based security solutions against threats targeting traditional networks and systems. This article investigates the use of SDN to detect and mitigate the risk of self-propagating ransomware. The infamous BadRabbit ransomware has been used for the proof of concept. To achieve this, an extensive analysis of BadRabbit was performed to identify its characteristics and understand its behaviour at both the infected device level and at the network level. As a result, several unique artifacts were extracted from BadRabbit, which could facilitate its detection. These artifacts were relied upon to design an SDN-based intrusion detection and prevention system. Our system comprises five modules, namely deep packet inspection, ARP scanning detection, packet header inspection, honeypot, and SMB checker. The first two modules have been inspired by other works and have been included for comparison with the existing solutions. Three other modules rely on novel SDN-based methods for ransomware detection. We have also evaluated the efficiency and the performance of our system in terms of detection time, CPU utilisation, as well as TCP and ping latency. Finally, the proposed approach has also been tested for other ransomware families, such as WannaCry and NotPetya. Our experimental results show that the system is effective in terms of detecting self-propagating ransomware and outperforms other proposed approaches.

As networks grow larger in size and complexity, their monitoring is becoming an increasing challenge because of the required polling performance and also due to heterogeneity of devices. As it turns out, SNMP (Simple Network Management Protocol) is by far the most popular monitoring protocol. However, due to the increase in the number of network devices, it becomes necessary to employ multiple SNMP managers, which is not cost-effective due to the hardware requirements. Additionally, the different proprietary SNMP implementations require custom configuration very often, as new devices are being incorporated into the network. Therefore, current SNMP managers not only require capabilities for large-scale monitoring but also a high degree of flexibility and programmability. In response, we propose an SNMP manager architecture with a flexible multi-threaded architecture, which effectively reduces the hardware resources necessary to poll the increasing number of SNMP agents. In addition, it features a scripting component to deal with the different data representations caused by proprietary implementations. Our experience has shown that SNMP agents can have high variability in their response times. Actually, our findings show a strong correlation between high response times and CPU load. As a solution, we propose and analyze novel adaptive polling algorithms that decrease the load on agents' CPUs while keeping the desired polling rate for fast agents. Finally, we present several real-world use cases where we show the benefits of the polling algorithms and the scripting component, by means of extensive measurement campaigns, This work was supported by Ayudas para la formación de doctores en empresas, Doctorados Industriales, under Grant DI-16-08979

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments.

The Internet of Everything (IoE) is the intelligent connection of people, process, data and things. In addition to machine-to-machine communications as facilitated in traditional networks and the Internet of Things, IoE enables machine-to-people and technology-assisted people-to-people interactions. An IoE local network may comprise various devices, including resource-constrained devices such as sensors, actuators, and embedded and wearable devices. The protection of such a network (and the data collected by and transmitted between network devices) against security attacks is highly challenging. First, the devices may be deployed in open environments without physical protection. More severely, the security protocols developed for traditional computer networks are ineffective or even inapplicable due to the resource-constrained nature of the end devices in IoE. In this article, we discuss the security challenges and requirements for data communications between IoE devices. We show that a suite of security protocols developed recently may provide an adaptive and resource-ef-ficient solution to IoE security.

Wireless Local Area Networks (WLANs) are one of the most popular networks for the Internet-of-Things (IoT) applications. Among various WLAN technologies, the Long-Range WAN (LoRaWAN) has gained a high demand in recent years because of its low power consumption and long-range communication. However, the Long-Range (LoRa) network suffers from interference problem among LoRa Devices (LDs) that are connected to the LoRa gateway by using the same Spreading Factors (SFs). In this article, we propose a game theory-based approach for estimating the time duration of transmission of data on suitable SFs such that interference problem is reduced and network devices maximize their utilities. We next propose a scheduling algorithm that schedules the allocated time duration on the SFs such that the waiting time of the network can be minimized. We finally use the network simulator-3 for validating the propose work. Various experiments are performed which demonstrate the improvement in the network performance.

Large-scale datacenters are the key infrastructures of cloud computing. Inside a datacenter, a large number of servers are interconnected using a specific datacenter network to deliver the infrastructure as a service (IaaS) for tenants. To realize novel cloud applications like the network virtualization and network isolation among tenants, the principle of software-defined network (SDN) has been applied to datacenters. In the setting, multiple distributed controllers are deployed to offer a control plane over the entire datacenter to efficiently manage the network usage. Despite such efforts, cloud datacenters, however, still lack a scalable and resilient control plane. Consequently, this paper systematically studies the coverage problem of controllers, which means to cover all network devices using the least number of controllers. More precisely, we tackle this essential problem from three aspects, including the minimal coverage, the minimal fault-tolerant coverage, and the minimal communication overhead among controllers. After modelling and analyzing such three problems, we design efficient approaches to approximate the optimal solution, respectively. Extensive evaluation results indicate that our approaches can significantly save the number of required controllers, improve the fault-tolerant capability of the control plane and reduce the communication overhead of state synchronization among controllers. The design methodologies proposed in this paper can be applied to cloud datacenters with other networking structures after minimal modifications.

Telecommunications service providers build highly available networks using equipment redundancy and overload protection mechanisms. However, because of network equipment misconfiguration or a natural disaster, the network equipment failover could be unsuccessful. This article proposes a linear programming model for determining the optimal distribution of user sessions in a commercial network such that the impact of an unsuccessful network equipment failover is minimal. The model has been applied successfully during the design of a commercial network that supports approximately 5 million customers. The proposed model will be useful for managing the telecommunication networks in a way that is more robust to catastrophic failure, providing greater security and better service to users. The article also presents quantitative results that show a modest increase in end-to-end one-way speech transmission delay ranging from 3.5 to 8.1 ms. This delay is also known as mouth-to-ear delay. The article proposes improvements to the linear programming model by putting an upper limit on the potential increase in the mouth-to-ear delay.

Unprecedented levels of device connectivity and the emergence of futuristic digital services are driving fundamental changes to underlying fixed and wireless data transport networks. Projected bandwidth requirements, coupled with increased network centralization and virtualization, will lead to the convergence of data-center, fixed and wireless systems, and a greater onus being placed on the optical routing/transport portion of these networks. Such converged networks will require the development of optical technologies capable of servicing a multitude of network user types. In this work, we propose the use of a Silicon Photonic (SiP) space-and-wavelength selective switch fabric as a flexible wavelength provisioning platform for converged optical networks. The envisaged converged network is presented and an experimental test-bed which demonstrates flexible Cloud Radio Access Networking (C-RAN), using a 4 × 4 micro-ring resonator based switch, is described. Successful provisioning and transmission of Wavelength Division Multiplexed (WDM) based analog Radio-over-Fiber (RoF) services, over 10 km of fiber, in the converged test-bed is demonstrated and evaluated in terms of the received Bit Error Rate (BER) and Error Vector Magnitude (EVM). Furthermore, the wavelength multi-casting capabilities of the SiP switch is shown to enable dynamic resource allocation in the optical domain, and this is highlighted through the experimental implementation and evaluation of two C-RAN use cases - representing high and low mobile traffic demand scenarios.

The SDN paradigm allows network management applications to configure and manage network devices directly. Researchers use simulators, emulation platforms, and experimental testbeds to evaluate ideas. Emulation and simulation platforms offer the advantages of more flexibility and larger scale; using a testbed produces more realistic and accurate results. Although they support SDN, the switches used in data centers pose an economic barrier for researchers. We propose DCLab that creates a low-cost reconfigurable SDN testbed using white-box switches. This letter explains the architecture of DCLab and reports experiments that demonstrate how it allows to overlay various logical topologies on a physical topology.

By decoupling network functions from dedicated, proprietary hardware network devices, Network Function Virtualization (NFV) allows building Virtual Network Functions (VNFs) that can run on standard, commodity servers to reduce cost and gain flexibility in network deployment, operation, and management. However, building VNFs with high-throughput and low-latency is a big challenge. In this paper, we propose eVNF - a hybrid fast-slow path architecture to build and accelerate VNFs with eXpress Data Path (XDP), which is a Linux kernel framework that enables high performance and programmable network processing. The programmability of XDP is limited to ensure kernel safety, thus causing difficulties when using XDP to accelerate VNFs. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP, e.g., in user-space. With the hybrid architecture, eVNF allows building fast and flexible VNFs. We applied eVNF to build four prototype VNFs: Flow Monitoring (eFM), Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB). These VNFs are evaluated individually and in service function chains (SFCs) using OpenStack. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage. eVNF-based VNFs also can scale out with the number of CPU cores and can combine with Open vSwitch - Data Plane Development Kit (OvS-DPDK) for better performance.

The rapid growth of communications and multimedia network services such as Voice over Internet Protocol (VoIP) have caused these networks to face a crisis in resources from two perspectives: 1. Lack of resources and, as a result, overload; 2. Redundancy of resources and, as a result, energy loss. Cloud computing allows the scale of resources to be reduced or increased on demand. Many of the gains obtained from the cloud computing come from resource sharing and virtualization technology. On the other hand, the emerging concept of Software-Defined Networking (SDN) can provide a global view of the entire network for integrated resource management. Network Function Virtualization (NFV) can also be used to virtually implement a variety of network devices and functions. In this paper, we present an energy-efficient framework called GreenVoIP to manage the resources of virtualized cloud VoIP centers. By managing the number of VoIP servers and network equipment, such as switches, this framework not only prevents overload but also supports green computing by saving energy. Finally, GreenVoIP is implemented and evaluated on real platforms, including Floodlight, Open vSwitch, and Kamailio. The results suggest that the proposed framework can minimize the number of active devices, prevent overloading, and provide service quality requirements.

Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices during their activity. In this work we focus on a new type of data, available thanks to the definition of the novel SRv6 paradigm, referred to as SRv6 Traffic Counters ( SRTCs ). SRTCs provide aggregated measurements related to forwarding operations performed by SRv6 routers. In this work a detailed description of different SRTCs types (SR.INT, PISD, PSID.TM and POL) is provided and their relationships is formalized. The theoretical framework deployed is used to identify, on the basis of network configuration parameters of both SRv6 and IGP protocols, the minimum set of independent SRTCs to characterize the Network Status : we show that about the 80% of counters can be neglected with no information loss. We also apply our framework to two use cases: i) Traffic Matrix (TM) Assessment and ii) Traffic Anomaly Detection. For the TM assessment, we show that in a partially deployed SRv6 scenario a specific type of SRTCs , i.e., PSID, is more reliable than other ones; on the contrary, in a fully deployed scenario POL and PSID.TM counters provide the full TM knowledge. For the Traffic Anomaly Detection case, we show that known solutions based on link load measurements can be improved when integrating SRTCs information.

Two key challenges of the PHY-MAC layers of the wireless body area network (i.e., WBAN) are - achieving high system capacity and ensuring secure coexistence. Besides capacity and coexistence, a WBAN PHY (i.e., physical) layer is required to fulfill some preconditions such as it should not impose any detrimental effect on the human health, the network devices must be small enough to be wearable, and it should be suitable for the small area coverage. On the other hand, besides coexistence, the MAC (i.e., Media Access Control) layer needs to ensure service priority, and fairness in resource sharing. Traditionally, the WBAN PHY-MAC is based on 2.4 GHz microwave. However, recently, 60 GHz mmWave based WBAN has been envisioned as a possible solution to the above mentioned challenges. In this paper we have conducted the quantitative comparisons between the 60 GHz and the 2.4 GHz based WBANs. We have also shown the superiority of the 60 GHz based PHY over the 2.4 GHz based PHY grounded on the derived mathematical formulas and simulation results in terms of the system capacity, per cell channel ratio, co-channel reuse ratio, network coverage, device miniaturization, and health issues. Furthermore, we have proposed a game theory based solution for the WBAN MAC layer to ensure coexistence, service priority, and fairness in resource sharing.

With the move from traditional hardware appliance based network functions to Network Function Virtualization, software development is decoupled from the hardware. However, as a network function is no longer optimized for hardware, beneficial features of networking hardware may not be used any more. Solutions such as SDN or NIC offloading aim to overcome this antipodes by integrating networking hardware into the packet processing pipelines. On the one hand, offloading traffic of network functions to hardware can increase throughput and reduce resource consumption. On the other hand, the number of parallel flows in a network can be very high, exhausting the capacity of the tables of the networking hardware and force the system to fall back to software processing. Fortunately, it is known that a large portion of the flows in the Internet are mice flows, whereas the majority of the traffic is constituted by elephant flows. If the elephant flows can be detected efficiently, the hardware tables can be used more efficiently as a larger share of the traffic can be offloaded. We introduce a machine learning based approach that takes its decision with the first packet of a flow. A fundamentally different approach is using packet sampling for the offloading decision. We are evaluating both approaches in terms of complexity, offloaded share of the traffic and table occupation. The results show that a machine learning based offloading decision is possible with the first packet. The sampling approach only reaches a comparable performance at very high sampling rates.

This article presents a coarse-grained reconfigurable cryptographic logic array named PVHArray and an intelligent mapping algorithm for cryptographic algorithms. We propose three techniques to improve energy efficiency without affecting performance. First, the coarse-grained pipeline variable reconfigurable operation units balance the system critical path delay and number of algorithm operations to ensure the best performance. Second, the hierarchical interconnect network overcomes the shortcomings of a single network, providing PVHArray with good interconnectivity and scalability while managing the network hardware resource overhead. Third, the distributed control network supports accurate period-oriented control with a lightweight hardware structure, preserving hardware resources for other performance enhancements. We combine these advances with deep learning to propose a type of smart ant colony optimization mapping algorithm to improve algorithm mapping performance. We implemented our PVHArray on a 12.25 mm2 silicon square with 55-nm CMOS technology, with each algorithm working at its optimum frequency. Experiments show that PVHArray improved performance by about 12.9% per unit area and 13.9% per unit power compared with the reconfigurable cryptographic logic array REMUS_LPP and other state-of-the-art cryptographic structures. For cryptographic algorithm mapping, our smart ant colony optimization (SACO) algorithm reduced compilation time by nearly 38%. Finally, PVHArray supports a variety of types of cryptographic algorithms.

This article reports an FPGA-based P4-enabled Smart NIC solution which is designed and implemented for web-scale cloud and to meet 5G/beyond 5G networking requirements. The P4-enabled Smart NIC solution leverages the open standards, platforms and software-defined approaches, responds to the real time Data Centre Networking service requests, in particularly, enables the end-to-end network slicing, which is one of the critical requirements of a multi-tenancy 5G network. We discussed the possibilities and challenges of P4 specification implementation in the FPGA to realise the Smart NIC functionalities. And after that, we showed its data plane programmability and flexibility with P4 features. Furthermore, we demonstrated its application scenario in an 5G environment mainly focusing on edge Data Centre to core Data Centre network slicing. The setup interconnects the P4-enabled Smart NIC with optical Bandwidth Variable Transponders, and the system offers agile 100 Gbps interface to transport the packets through P4-defined data plane for L2/L3/L4 parsing and action. The P4-enabled Smart NIC can change the data plane pipelines in seconds, and it can achieve maximum 84.8 Gbps throughput. With P4 programmed hardware offloaded Segment Routing can produce 30% more bandwidth than without.

Fog computing has emerged as a unifying platform to provide computing, communication, and storage for a variety of mobile applications. That helps achieve high bandwidth, high intelligence, low latency, and low energy consumption in handling massive networking devices and emerging rich multimedia services in 5G networks. Current prominence and future promises are changing from the Internet of Things (IoT) to the Internet of Everything (IoE), which is a union of people, process, data, and things. However, the development of fog radio access networks (F-RANs) is challenged by the diversity of IoE, ultra-high-definition videos on demand from users, and low-latency requirement of heterogeneous IoT devices. In this article, we present an architecture of visual IoE (V-IoE) in F-RANs. We systemically analyze the key challenges of V-IoE from the perspective of F-RANs, and propose a crowd V-IoE architecture. Through experimental results, we demonstrate that our proposed architecture exhibits better performance with lower bandwidth requirement, lower energy consumption, and lower latency in F-RANs. Finally, we conclude with a discussion of potential directions.

As the number of network devices is increasing and they are highly connected, network attacks have become more complex and varied. To mitigate these attacks, multiple types of network security equipment are used in combination, requiring considerable security knowledge of each type of network security equipment. Also, the deployment of network security equipment requires considerable cost and time. To solve these problems, this article proposes an IBCS based on automation and virtualization. IBCS provides not only an automated, virtualized security system for security service providers, but also easy, quick, and flexible cloud-based security services for security service consumers based on their intents. Finally, this article demonstrates the feasibility of IBCS with a prototype in a real network environments.