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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.

We present a novel dual-DBA allocation, with a fast P4-enabled scheduler to provide low latency upstream grant allocations. We show latency reduction of 37% and 43%, respectively, compared to standard and virtual PONs.

Fingerprinting is a process of identifying the remote network devices and services running on the devices, including operating systems (OS) of the devices, and hosts running different OSs. Several research proposals and commercial products are available in the market to defeat fingerprinting. However, they have performance limitations and expose themselves to attackers. In this paper, we utilize some real-time fault-tolerance concepts (viz. real-time/dynamic, detection/locating, confinement/localizing and masking/decoy) to propose a plug-and-play adaptive middleware architecture called Secure Dynamic Middleware (SDMw) with a view to defeat attackers fingerprinting the network, without exposing itself to the attackers. We verify that the proposed scheme works seamlessly and requires zero-configuration at the client side.

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.

Single-root I/O virtualization (SR-IOV) allows virtual machines direct access to physical network cards through so-called virtual functions (VFs), considerably reducing networking overhead compared to paravirtualized interfaces by avoiding the hypervisor’s network stack. However, the maximum number of VFs on each card is often limited compared to the number of VMs running on each host, leading to the problem of choosing which VMs to allocate these VFs. In this paper, we introduce FlexVF, a mechanism for dynamically allocating and switching between VFs and paravirtualized networking on VMs based on network activity monitoring. We show that FlexVF improves VM network performance by 75% without affecting network operation.

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.

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

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.

The diverse and stringent requirements of 6G networks, detailed in the present book, give rise to fully flexible, end‐to‐end, software‐defined network paradigm, where every part of the network can be configured and controlled via software. However, the wireless environment per se – i.e. the wireless medium or channel – is generally assumed uncontrollable, thus, imposing inherent limitations to 6G networks, that arise by the very nature of wireless operation. To this end, the emergence of the newly introduced concept of reconfigurable intelligent surfaces (RISs) challenges the fundamental status quo that the wireless environment is fixed by nature, paving the way for the realization of end‐to‐end fully reconfigurable 6G networks.RISs are network devices that can control and manipulate the radio waves traversing through the wireless channel. Since RISs reside within the wireless channel and not at the communication endpoints, they can control the wireless channels from within. In this context, the present chapter introduces the concept of RISs within the 6G framework, elaborating on their advantages and limitations. RISs are then compared with other transmission technologies, e.g. phased arrays, multi‐antenna transmitters, and relays, while demonstrating the trade‐offs governing their operation and applicability. Subsequently, the different types of RIS implementations are presented from a theoretical standpoint, along the available prototypes in the literature; thus, demonstrating how the RIS technology is already a reality, ushering wireless networks into the 6G era.

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.

The global community networking movement promotes locally-managed network infrastructure as a strategy for affordable Internet connectivity. This case study investigates a group of collectively managed WiFi Internet networks in Argentina and the technologists who design the networking hardware and software. Members of these community networks collaborate on maintenance and repair and practice new forms of collective work. Drawing on Actor-Network Theory, we show that the networking technologies play a role in the social relations of their maintenance and that they are intentionally configured to do so. For technology designers and deployers, we suggest a path beyond designing for easy repair: since every breakdown is an opportunity to learn, we should design for accessible repair experiences that enable effective collaborative learning.

A partir de la pandemia suscitada en el año 2020 se han tenido que postergar varias actividades de diferentes sectores, mientras que otras han migrado de la modalidad presencial a la virtual. La educación es una de estas que ha tenido que cambiar, aunque en un inicio fue complicado adaptarse a la modalidad online, muchas aplicaciones han ayudado en este proceso, estas aplicaciones, de hecho, han llegado a tener un mayor auge a causa de ello, una de esas plataformas es Cisco Packet Tracer. Dicho programa mencionado anteriormente permite realizar simulaciones de redes empresariales, ya que, dentro de la plataforma se pueden utilizar distintos dispositivos de hardware, al igual que permite realizar la configuración de cada uno de esos dispositivos, por lo cual, la herramienta es de gran importancia, tanto para las universidades, como para los estudiantes, ya que permite estudiar a mayor profundidad cada uno de los dispositivos de red que emula la aplicación.

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.

Software Defined Networks (SDNs) is an emerging concept in network architectures, which divides the network operations into two, control and data, layers. In this concept, control and management operations are moved from the network devices to the controller and inside the control plane. This separation makes it possible to incorporate network devices for different applications, while on the other hand leads to vulnerabilities in the network. In fact, the controller becomes the bottleneck of the network, and it is vulnerable to intrusions. Various approaches have been proposed to detect intrusion in these networks, which among them using deep learning methods has gained the majority of attention in the past decade. In this paper, an intrusion detection system based on the SDN model is presented which is executed as an application module in the controller. The proposed system consists of three phases: in the first phase, for pre-training, sparse stacked auto-encoders are incorporated which learn the features in an unsupervised manner. In the second phase, to train the system, the SoftMax classifier is used and in the third phase, system parameters are optimized. Performance of the proposed system is evaluated according to two datasets, namely NSL-KDD and CICIDS2017, for classification of attacks. To implement the proposed method, the Mininet software and Keras framework, which is based on Tensorflow, are incorporated. The average accuracy in detection and classification of attacks using the proposed method is 98.5%, which is promising in comparison with previous methods.

Data centers are located centralized to do computation and accessing huge amount of data by the network devices which are interconnected to form the network path. Servers are stacked, data storage is placed in them. Data server backup and server redundancies are the recovery mechanisms implemented. Data centers compute, store, distribute the data by processing them and the data center controls all the interconnected network equipment in the distributed network. In current, RAID system is implemented to avoid the service disruptions due to disk failures, the availability of system and services are achieved with this expensive model. But still the availability is lost, and service disruptions happen due to disk failures, the machine learnings models to be used to predict the disk failures well in advance. Data center has increased usage of system with increased data storage, the failure in disc makes the system failed and down time increases. Analysis on the methods of problems in disk and methods of disk availability and predict the disk failure is the main goal. Various machine learning models are identified and discussed along with the SMART parameters for measuring the failure of the disk. Improved method of Ensembling of trees, random forest and boosting techniques are also discussed.

Social Opportunistic IoT (Social OppIoT) networks is a subclass of Social Internet of Things (SIoT) networks. In social OppIoT, users perform communication in a distributed manner using smart devices by regularly moving around without any communication infrastructures, making routing a strenuous process due to its highly fragile connection intermittency and device mobility. Moreover, due to the growing heterogeneous devices, problems can exist in searching for the right relay node from a massive number of devices. In this paper, a novel forwarding scheme named “A Local Betweenness Centrality Based Forwarding Technique for Social Opportunistic IoT Networks” (LBCFT) has been proposed, which uses a reduction strategy to discard the inefficient devices. LBCFT introduces a new centrality measurement called local betweenness centrality to form the significant overlapping communities of network devices to boost forwarding. Message dissemination is controlled by handling inefficient devices using a reduction strategy, which includes the node’s trajectory and intra-community and inter-community centrality. The performance of LBCFT is evaluated through ONE Simulator against the existing analogous ideologies like Supernode, Geo-Routing with Angle-based Decision (GRAD), and the benchmark protocols BubbleRap, as well as PROPHET. The simulation results show that the proposed LBCFT protocol, on average, outperforms Supernode, GRAD, BubbleRap, and PROPHET by 5.22%, 36.51%, 64.12%, and 57.96% respectively, in terms of the delivery probability.

Distributed denial-of-service (DDoS) attacks are responsible for shutting down servers, denying access to critical sectors of the economy, and generating substantial downtime costs and reputation harm. Only in 2020, over ten million DDoS attacks were observed worldwide. Commonly, open-loop network traffic rate policing is used to mitigate them. That is how network devices are currently designed. This paper shows how to extend the state-of-the-art design by introducing the adaptive closed-loop tuning of policing mechanisms. As demonstrated experimentally, open-loop policing based on the celebrated token-bucket mechanism generates a steady-state control error. In contrast, the robust self-tuning controller eliminates that control error while adjusting the bitrate limiting operations to the severe and hardware-specific network operating conditions during a DDoS attack. The study shows the controller’s implementation details and discusses the critical difficulties encountered in its technical development. Furthermore, it illustrates how the control error variance depends on the commanded traffic rate limit and explains why the self-tuning controller’s anti-windup filter may fail to bring the control signal back to the set of admissible control values. All experiments presented in this paper were conducted using real data from the Polish nation-wide cybersecurity system FLDX managed by the NASK National Research Institute.

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.

Software Defined Networking (SDN) is a new networking paradigm where forwarding hardware is decoupled from control decisions. It promises to dramatically simplify network management and enable innovation and evolution. In SDN, network intelligence is logically centralized in software-based controllers (the control plane), while network devices (OpenFlow Switches) become simple packet-forwarding devices (the data plane) that can be programmed via an open interface (OpenFlow protocol). Such decoupling of the control plane from the data plane introduces various challenges that include security, reliability, load balancing, and traffic engineering. Dreadful security challenges in SDNs are denial of service (DoS) and distributed denial of service (DDoS) attacks. For instance, in SDNs, DoS/DDoS attacks could flood the control plane, the data plane, or the communication channel. Attacking the control plane could result in failure of the entire network, while attacking the data plane or the communication channel results in packet drop and network unavailability. In this paper we deliver several contributions that shed light on the field of DoS/DDoS attacks in SDNs, providing a complete background about the area, including attacks and analysis of the existing solutions. In particular, our contributions can be summarized as follow: we review and systematize the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches. Moreover, the discussed countermeasures are organized accordingly to their focus, be it on detection, mitigation, prevention, or graceful degradation. Further, we survey the different approaches and tools adopted to implement the revised solutions. Finally, we also highlight possible future research directions to address DoS/DDoS attacks in SDNs.

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.

Cross-border trading platforms study logistics from the perspective of the network, involving all links in the network and the supply chain. The network status is constantly changing, and the acquisition and analysis of the real-time status of each link on the transaction path can further improve the accuracy of the optimal path selection. Meanwhile, traditional networks can no longer adapt to the rapid development of today’s network technology and applications due to their rigid structure and simplified functions. As for traditional networks and their associated network equipment, problems such as inconvenient management and monitoring, poor interoperability, difficulty in expanding network functions, and high operating costs have severely hindered the efficiency of network functions and the pace of innovation. In order to solve many problems faced by traditional networks, in the paper, an SDN network path selection algorithm based on the dual impact factors of cloud service nodes is proposed. In the algorithm proposed in the paper, the final selected path is considered to be composed of multiple links. First, a cloud environment based on the Ryu controller is build, and a corresponding data collection strategy is constructed to complete the acquisition of the link available bandwidth and delay that affect the real-time status of the path. Then, the weighted results are calculated according to the proposed candidate path weight distribution formula to realize the sorting and screening of each transaction link, and provide support for the selection of the cross-border transaction system service path with load balance. The simulation results show that compared with the traditional DLB algorithm, the algorithm proposed in the paper has better load balancing performance, which further shows that the proposed algorithm has a better choice for the path.

This chapter introduces and describes the organization and differences between the telecommunication networks, systems, and services. It also includes a section on the fundamental aspects of telecommunication media key for the Smart Grid, namely optical fiber, radio, and power lines. Telecommunication services delivery is the goal of telecommunication networks or systems. Transmission media used in telecommunications are classified into physical and nonphysical. Telecommunication networks spread over territories to connect end users among them, to provide telecommunication services. The traditional world of telecommunication networks was organized around Backbone and Access networks. Any modern telecommunication system combines transport and switching/routing functions in its network devices. The protocol architecture of a telecommunications system is commonly arranged in three separate “planes” to serve different purposes: user plane, control plane, and management plane. Power lines are the supporting medium for the utility communications that travel inside the electricity cables, that is PLC.

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus + , to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus + directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus + introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus + is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus + achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems.

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.

Growth in multimedia traffic over the Internet increases congestion in the network architecture. Software-Defined Networking (SDN) is a novel paradigm that solves the congestion problem and allows the network to be dynamic, intelligent, and it centrally controls the network devices. SDN has many advantages in comparison to traditional networks, such as separation of forwarding and control plane from devices, global centralized control, management of network traffic. We design a policy-based framework to enhance the Quality of Service (QoS) of multimedia traffic flows in a potential SDN environment. We phrase a max-flow-min-cost routing problem to determine the routing paths and presented a heuristic method to route the traffic flows in the network in polynomial time. The framework monitors the QoS parameters of traffic flows and identifies policy violations due to link congestion in the network. The introduced approach dynamically implements policy rules to SDN switches upon detection of policy violations and reroutes the traffic flows. The results illustrate that the framework achieves a reduction in end-to-end delay, average jitter, and QoS violated flows by 24%, 37%, and 25%, respectively, as compared to the Delay Minimization method. Furthermore, the proposed approach has achieved better results when compared to SDN without policy-based framework and reduced end-to-end delay, average jitter, and QoS violated flows by 51%, 62%, and 28%, respectively.

P4 is a domain-specific language to develop the packet processing of network devices. These programs can easily hide errors, therefore we give a solution to analyze them and detect predefined errors in them. This paper shows the idea, which works with the P4 code as a set of components and processes them one by one, while calculating their pre- and postconditions. This method does not only detect errors between the components and their connections, but it is capable to reveal errors, which are hidden in the middle of a component. The paper introduces the method and shows its calculation in an example.

This paper makes use of the new architecture software-defined network (SDN) in the cloud data center based on P4 language to realize the flexible management and configuration of the network equipment to achieve (a) data center virtualization management and (b) data center resource optimization based on the P4 programming language. Furthermore, error tolerance of dynamic network optimization depends on the virtual machine (VM) online migration technology, and the load balancing mechanism has a very good flexibility. At the same time, the paper proposed a multipath VM migration strategy based on a quality of service (QoS) mechanism, which divides the VM migration resources into different QoS flows by network dynamic transmission and then selects valid forwarding for each flow path to migrate VMs. This ensures to improve the overall migration performance of VMs and ultimately the dynamic optimization of the network resources and their management. Our experimental evaluations show that the proposed model is approximately 13% and 17% better than the traditional state-of-the-art methods in terms of minimum migration time and the least downtime, respectively.

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

Situation during COVID19 pandemic, requires all schools to conduct online learning. This condition is done to prevent the spread of pandemic. The problem is that the infrastructure is not fully supported. Learning in marginal areas is still constrained by the difficulty of the signal or internet connection. This research was conducted to obtain an infrastructure design that can be used in areas with minimal internet connection, or even no internet network connection. Using R&D, the research begins by designing a network device on the school side – Acces Point, also a tool to expand coverage - repeater. Furthermore, design and testing of signal amplifier antennas that can be used on the client side is carried out. For the client side, several types of amplifier antennas are made that can be used to receive Wifi frequencies. There are three types of antennas that are made and tested, namely Yagi, Yagi Cross, and cantennas. Both are Unidirectional antenna. Connection quality is measured with a Wifi analyzer device to determine network quality. As for mobile phones, it is measured using the Wifi Master Key. The result obtained is a WELAN infrastructure design which is equipped with unidirectional antenna design with a total range of 8 Km. &nbsp, Keadaan pada masa pandemi COVID19, mengharuskan semua sekolah untuk melakukan pembelajaran daring. Hal ini dilakukan untuk mencegah meluasnya pandemi. Hal yang menjadi permasalahan adalah infrastruktur yang ternyata belum sepenuhnya mendukung. Pembelajaran di daerah marginal, masih terkendala sulitnya sinyal atau jaringan internet. Penelitian ini bertujuan untuk mendapatkan satu disain infrastruktur yang dapat digunakan di daerah yang minim koneksi internetnya, atau bahkan tidak terdapat jaringan internet. Metodologi yang digunakan adalah R&D, dimulai dengan merancang perangkat jaringan di sisi sekolah yang berupa akses point, serta alat untuk memperluas jangkauan berupa Wifi extender/repeater . Selanjutnya dilakukan perancangan dan uji coba antena penguat sinyal yang bisa digunakan disisi klien (baik guru, maupun siswa). Untuk sisi klien, dibuatkan beberapa jenis antena penguat yang dapat digunakan untuk menerima frekuensi gelombang Wifi. Ada tiga jenis antena yang dibuat dan di uji coba, yaitu Yagi, Yagi Silang, dan antena kaleng. Ketiga antena dari jenis Unidirectional antenna. Kualitas koneksi diukur dengan perangkat Wifi analyzer untuk mengetahui kualitas jaringan. Sedangkan untuk hand phone, diukur dengan menggunakan Wifi Master Key. Hasil yang didapatkan adalah satu disain infrastruktur WELAN (Wireless Extended LAN) yang dilengkapi disain antenna unidirectional dengan jangkauan total 8 Km.

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.

This study aims to build a network automation infrastructure by implementing a Software Defined Networking (SDN)-based web application to facilitate network administrators in implementing a web-based controller system for network devices by optimizing the configuration, management and operation time of network devices. This research uses action research method. In the action research method, researchers can describe, interpret and explain a condition at the same time as carrying out interventions aimed at improvement or participation. The results of this study indicate that the design and implementation of a Software Defined Networking (SDN)-based web application for network device automation using the python programming language with the paramiko library and the django framework can be applied to network infrastructure, so that with the application of SDN on network device automation applications can perform controllers. centralized network devices, so that configurations of many routers can be optimally carried out on a single Software Defined Networking (SDN) based web application

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Symmetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarm-like normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.

High level, network programming languages, like P4, enable students to gain hands-on experience in the structure of a switch or router. Students can implement the packet processing pipeline themselves, without prior knowledge of circuit design. However, when choosing a P4 programmable target for use in the classroom, instructors face a lack of options. On the one hand, software solutions, such as the behavioral model (BMv2) switch, are overly simplified and offer low performance. On the other hand, existing hardware solutions are closed source and expensive. In this paper, we present P4Pi, a new, low-cost, open-source hardware platform intended for networking education. P4Pi allows students to design and deploy P4-based network devices using the Raspberry Pi board, which has a price tag of less than many academic textbooks. We describe the high-level design of the P4Pi platform, offer some suggestions for how P4Pi could be used in the classroom, and present some additional use-cases for applications and functionality that could be developed using P4Pi.

As All-IP studios leveraging the SMPTE ST 2110 document suite gain traction, one of the fundamental pillars upon which such systems rely is an accurate and reliable time transfer protocol. SMPTE ST 2059 standardized such a model based on the IEEE 1588 Precision Time Protocol (PTP). Every element, whether it be network or media devices, impacts the overall time transfer accuracy. This impacts the timing of the ST 2110 media streams that are generated or received by the media nodes. While PTP does provide fault tolerance capabilities, performance degradation of time transfer is not always well understood. This can significantly impact operations in live networks, it being due to a sudden event or a degradation over time. Therefore, an overarching view of the end-to-end timing system including backup, standby, and network devices is required. Monitoring of such capabilities should be common to all devices, whenever possible, including different means to verify in-band and out-of-band measurements as to how devices are performing, with respect to time transfer. Combined with historical archiving of the monitored data, this provides the framework for tracking and correlating possible events. This paper describes the different monitoring capabilities, their advantages and weaknesses, and the value they provide to the monitored datasets. It further focuses on the means to make the process uniform across all monitored devices to ensure consistency in the collected data. Finally, we discuss the ongoing efforts in SMPTE and other standardization bodies around PTP monitoring, the common trends, expected outcomes, and how these will contribute to streamlining time transfer monitoring, supervision, and analysis .

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.

Проблематика. Принципи побудови і функціонування мереж SDN суттєво відрізняються від традиційних телекомунікаційних мереж. Для визначення чисельних значень показників функціонування такого типу мереж та перевірки їх відповідності вимогам, консорціумом OpenNetworkingFoundation розроблений мережевий емулятор Mininet. Елементи мережевого емулятора є у відкритому доступі. Однак, побудова імітаційної моделі і порядок визначення показників функціонування мережі SDN з використанням Mininet має ряд особливостей,які розглянуті в роботі. Мета дослідження. Створення імітаційної моделі на базі мережевого емулятору Mininet і визначення показників функціонування мереж SDN різної структури. Методика реалізації. Визначення показників функціонування мережі SDN у режимі перевантаження напрямків зв'язку. Результати дослідження. Проведено моделювання мереж SDN складної структури і визначені наступні показники функціонування: RTT (RoundTripTime) для кожного напрямку зв'язку, пропускна здатність гілок і напрямків зв'язку, величина затримки на мережевих елементах, завантаження портів OpenFlowSwitch, елементи мережі з найбільшою затримкою, число обслугованих і втрачених пакетів. Висновки. Використання мережевого емулятора Mininet є досить зручним інструментом для визначення показників функціонування мереж SDN. Однак, є і проблеми. По-перше, налаштування працездатності системи вимагає написання програм для забезпечення взаємодії стандартних елементів з бібліотеками Mininet. По-друге, при виникненні перевантажень в напрямках зв'язку, FloodlightController не забезпечує автоматичного балансування трафіку по обхідним шляхам. Цю задачу доводиться вирішувати в ручному режимі шляхом внесення змін до таблиці маршрутизаціїOpenFlowSwitch. Background. The implementation of the new modern services on existing networks requires replacement or modernization of the old equipment. This negatively affects the efficiency of providing users with new types of services and their cost. Therefore, the issues/tasks of implementing SDN technology in the construction of networks are in the center of attention of representatives of science-research organizations and telecom operators. SDN networks have the advantages of flexible scaling of the system without replacing existing server and network equipment, eliminating operator dependence on specific vendor solutions, and rapid implementation of the new network technologies and services. To determine the numerical values of indicators of the functioning of SDN networks and check their compliance with the requirements, it is proposed to use in working process simulation modelling. For these goals, the Open Networking Foundation consortium developed the Mininet network emulator, the elements of that are published publicly. The formation of the simulation model and the procedure for determining the performance indicators of the SDN network that uses Mininet has a number of features that are considered in the work. Objective. The purpose of the article is to build a simulation model based on the Mininet network emulator and to determine the performance indicators of SDN networks of various structures/topologies. Methods. Creation of a SDN network segment for testing process of its functioning in the overload mode of various communication routes. Results. The Mininet network emulator allows simulating SDN networks of a rather complex structure, change the performance of network branches and the amount of load in communication routes, to create a network-wide overload mode. During the simulation, it is possible to determine a number of indicators of network performance, such as the RTT (Round Trip Time) parameter for each route of communication, the bandwidth of branches and routes of communication, the amount of delay on network elements, loading of OpenFlow Switch ports/interfaces, network elements with the highest delay, the number of served and lost packets. Conclusions. Use of the Mininet network emulator is a fairly convenient tool for determining the performance indicators of SDN networks. However, there are also some problems. First of all, setting up the system's operability requires writing programs to ensure the interaction among standard elements from the Mininet library. Secondone, is when congestion occurs in the communication routes, the Floodlight Controller does not automatically balance traffic along the work-around route. This task has to be solved manually by making changes to the OpenFlow Switch routing tables.

Діяльність Збройних Сил характеризується специфічними вимогами до інформації, до засобів зв’язку та передачі даних. Аналіз сучасного світового досвіду показує, що успішне проведення військових операцій вимагає своєчасного комплексного інформаційного забезпечення бойових дій, що вже неможливе без впровадження сучасних інформаційних технологій. Для забезпечення зв’язку в умовах впливу деструктивних зовнішніх чинників і відсутності традиційної телекомунікаційної інфраструктури потрібні мережі передачі інформації, що мають швидке розгортання, автономність електроживлення кожного вузла, високу живучість, здатність передачі інформації при випадкових процесах переміщення, знищення, включення і виключення вузлів. Все це можливо завдяки використання мобільних Ad-Hoc мереж з децентралізованої структурою (Mobile Ad-Hoc Networks, MANET) [1, 2]. Основними перевагами побудови MANET є: реалізація децентралізованого управління компонентами мережі; відсутність фіксованих вузлів; здатність кожного вузла виконувати функції маршрутизатора. Завдяки вказаним перевагам мережі MANET мають перспективи застосування для забезпечення зв’язку в тактичній ланці управління, та забезпечать мобільним абонентам можливість безперервного і стійкого обміну інформацією під час знаходження в рухомих об’єктах (КШМ, бронетехніці, автомобілях) або переміщенні пішим порядком. Проблема забезпечення якості обслуговування в Ad-Hoc мережах, була і залишається актуальною для розробників протоколів, мережевого устаткування і кінцевих користувачів. На всіх рівнях мережі активно використовуються механізми буферизації і управління чергою пакетів, в тому числі і адаптивні, покликані, з одного боку, обслуговувати сплески трафіку з мінімальними втратами пакетів, а з іншого, – забезпечити достатню смугу пропускання і прийнятні тимчасові затримки. В статті досліджені процеси в тактичній радіомережі з розробленим нечітким регулятором на основі інтерактивної системи MATLAB. Мережа представлена замкненою системою автоматичного керування (системою з активним управлінням чергою) зі змінними параметрами (змінною кількістю комунікаційних каналів чи кількістю сесій TCP N(t) та часом проходження пакетів туди й назад R(t)). Застосування розробленого нечіткого регулятора в тактичній радіомережі є ефективним, дозволяє утримувати поточну довжину черги, близькою до бажаної та ефективну швидкість передачі даних достатню для практичного використання.

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.