Experimental Verification on a Slicing Management Based on the mPlane Monitoring Plane with GPON Comparison

ABSTRACT

We experimentally verify a slicing approach, operating in a PBB-TE core and PON access architecture, based on the mPlane measurement plane, also adopted to manage slicing instances. The key element of this paper is the proposal and the implementation of the Slicing End Point (SEP), that is the client element to set up the slice path, and, at the same time, it allows us to monitor the network performance. The slices are based on VLAN/MPLS/PBB-TE logical paths and they can be managed for the implementation of different class of services. The whole SDN mPlane architecture allows us manage the slices also with restoration procedures. Furthermore, the tests on latency confirm the advantages of the NG-PON2 accesses for 5G backhauling also in this slicing configuration.     

A Measurement Plane for Optical Networks to Manage Emergency Events

In this work, we show a wide geographical area optical network test bed, adopting the mPlane measurement plane for monitoring its performance and to manage software defined network approaches, with some specific tests and procedures dedicated to respond to disaster events and to support emergency networks. Such a test bed includes FTTX accesses, and it is currently implemented to support future 5G wireless services with slicing procedures based on Carrier Ethernet. The characteristics of this platform have been experimentally tested in the case of a damage-causing link failure and traffic congestion, showing a fast reactions to these disastrous events, allowing the user to recharge the initial QoS parameters.     

基于SDN的TDMA体制星间网络架构设计

TDMA(时分多址)体制的导航星间网络既可以实现导航测距,也具备较高的数据传输速率,具有较为广泛的业务适应能力。然而,TDMA体制的星间网络系统也存在着星上处理复杂与卫星节点处理能力低的矛盾。借鉴SDN(软件定义网络)将网络系统控制层面与数据层面相分离的思想,在TDMA体制的星间网络中引入SDN技术,设计了基于SDN的TDMA体制星间网络架构,将控制功能从卫星节点抽离出来,使其可以专注于星间数据转发,控制管理信息主要由卫星地面站(后续为高轨道卫星)扮演的SDN控制节点制定并分发,从而简化了卫星的业务负担,同时可以借鉴成熟的地面网络技术制定高效的控管策略。对所设计架构的主要的控管流程进行了仿真模拟,仿真结果表明,该架构具有一定的可行性。     

Enabling Optical Network Test Bed for 5G Tests

In this work, we show some experimental approaches concerning optical network design dedicated to 5G infrastructures. In particular, we show some implementations of network slicing based on Carrier Ethernet forwarding, which will be very suitable in the context of 5G heterogeneous networks, especially looking at services for vertical enterprises. We also show how to adopt a central unit (orchestrator) to automatically manage such logical paths according to quality-of-service requirements, which can be monitored at the user location. We also illustrate how novel all-optical processes, such as the ones based on all-optical wavelength conversion, can be used for multicasting, enabling development of TV broadcasting based on 4G–5G terminals. These managing and forwarding techniques, operating on optical links, are tested in a wireless environment on Wi-Fi cells and emulating LTE and WiMAX systems by means of the NS-3 code.     

DRL-M4MR: An intelligent multicast routing approach based on DQN deep reinforcement learning in SDN

Traditional multicast routing methods have some problems in constructing a multicast tree. These problems include limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multiobjective optimization problem, and DRL-M4MR, an intelligent multicast routing algorithm based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in a software-defined network. First, combining the characteristics of SDN global network-aware information, the multicast tree state matrix, link bandwidth matrix, link delay matrix and link packet loss rate matrix are designed as the state space of the reinforcement learning agent to solve the problem in that the original method cannot make full use of network status information. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree in four cases. Third, single-step and final reward function forms are designed to guide the agent to make decisions to construct the optimal multicast tree. The double network architectures, dueling network architectures and prioritized experience replay are adopted to improve the learning efficiency and convergence of the agent. Finally, after the DRL-M4MR agent is trained, the SDN controller installs the multicast flow entries by reversely traversing the multicast tree to the SDN switches to implement intelligent multicast routing. The experimental results show that, compared with existing algorithms, the multicast tree constructed by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Code and DRL model are available at https://github.com/GuetYe/DRL-M4MR.     

Using dithering for implementing geometric moment function estimation in a correlation retina

Correlation retinas measure the correlation product of an image projected on a sensor by optical means and a function f(x, y) stored in the retina. This optical correlation is well suited for the measurement of geometric moments, and this is why one can find in the literature several correlation retina circuits dedicated to it. Unfortunately, these architectures are not programmable and are dedicated to specific applications. Moment functions are of great interest in pattern recognition. This application needs to compute, for example, geometric moments whose order can depend on the application. Thus, the implementation of this function in a correlation retina requires a flexible architecture where the function f(x, y) can be modified to allow the measurement of the product of the image under analysis and f(x, y). The most robust solution is to memorize f(x, y) in memory devices distributed in the array of pixels constituting the retina. But, for technological problems, it is necessary to limit the number of bits used to store f(x, y) in the sensor. In this article, we propose to use dithering algorithms to code f(x, y) on only one bit. We present herein the architecture of the retina circuit on which we are currently working and show that it is possible to obtain approximate geometric moment values with it. The text was submitted by the authors in English.     

Ising models on hyperbolic graphs

We consider Ising models on a hyperbolic graph which, loosely speaking, is a discretization of the hyperbolic planeH ² in the same sense asZ ^d is a discretization ofR ^d . We prove that the models exhibit multiple phase transitions. Analogous results for Potts models can be obtained in the same way.     

Freeq-Schrödinger equation from homogeneous spaces of the 2-dim Euclidean quantum group

After a preliminary review of the definition and the general properties of the homogeneous spaces of quantum groups, the quantum hyperboloidqH and the quantum planeqP are determined as homogeneous spaces ofF _q (E(2)). The canonical action ofE _q (2) is used to define a naturalq-analog of the free Schrödinger equation, that is studied in the momentum and angular momentum bases. In the first case the eigenfunctions are factorized in terms of products of twoq-exponentials. In the second case we determine the eigenstates of the unitary representation, which, in theqP case, are given in terms of Hahn-Exton functions. Introducing the universalT-matrix forE _q (2) we prove that the Hahn-Exton as well as Jacksonq-Bessel functions are also obtained as matrix elements ofT, thus giving the correct extension to quantum groups of well known methods in harmonic analysis.     

基于SDN架构的电力通信网络质量感知技术研究

电力工业是国民经济的基础支柱,其通信网络是电力生产、经营和管理的一个重要组成部分。针对现通信网中业务流实时监测的难题,提出引入一种基于SDN架构设计的新型流量感知监测技术方案,经实际网络延迟数据与RLI算法、MDI指标中的DF参数、VTD指标互作对比。为此搭建基于NetMag可编程网络设备的实验环境通过验证及建模对比,均达到预期效果。论证该项质量感知技术能为电力通信网提供可视化、实时、智能的运维平台作部署,这将使运维管理工作更加主动、有效,有利于提高网络运行的效率与稳定性。     

Entanglement distribution in star network based on spin chain in diamond

After star network of spins was proposed, generating entanglement directly through spin interactions between distant parties became possible. We propose an architecture which involves coupled spin chains based on nitrogen-vacancy centers and nitrogen defect spins to expand star network. The numerical analysis shows that the maximally achievable entanglement $E_m$ exponentially decays with the length of spin chains M and spin noise. The entanglement capability of this configuration under the effect of disorder and spin loss is also studied. Moreover, it is shown that with this kind of architecture, star network of spins is feasible in measurement of magnetic-field gradient.     

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.     

An efficient packet parser architecture for software-defined 5G networks

Software-defined networks (SDN) has emerged with the capability to program in order to enhance flexibility, management, and testing of new ideas in the next generation of networks by removing current network limitations. Network virtualization and functionalization are critical elements supporting the delivery of future network services, especially in 5G networks. With the integration of virtualization and functionalization, network resources can be provisioned on-demand, and network service functions can be composed and chained dynamically to cater to various requirements. 5G networks are expected to rely heavily on SDN, which has been widely applied in core network design. To have a software-defined 5G network, not only is new spectrum and interface needed from SDN, but also a programmable and efficient hardware infrastructure is required. Admittedly, hardware components and infrastructure play an important role in supporting 5G networks. In other words, the software-defined 5G network data plane must have the required flexibility and programmability to support upcoming needs and technologies. Technological solutions need to respond to actual requests in infrastructure. Packet parsers in the data plane of software-defined 5G networks are one of the most important components because of the variation in the type of network headers and protocols. Each SDN switch needs to identify headers for processing input packets in the data plane, where the packet parser operates. Multiple implementations of packet parsers have been done on different substrates that occupy large hardware resources and areas on chip. However, they are not suitable for software-defined 5G networks. Certain architectures have been presented for packet parsing, aimed at accelerating the process of header parsing, however no attention has been paid toward reducing the area and the volume of the needed hardware resources and programmability in the data plane. This paper presents a new and efficient architecture for packet parsers on Field Programmable Gate Arrays (FPGA), called Efficient FPGA Packet Parser (EFPP) in a designed software-defined 5G network. This architecture emphasizes the removal of Ternary Content Addressable Memory (TCAM) to decrease hardware resources and efficiency in the data plane. Moreover, this architecture uses the chip’s processing speed and reconfiguration capabilities to support new protocols and network headers while maintaining flexibilities on software-defined 5G networks. EFPP is applied to chips on FPGA Xilinx ZedBoard Zynq, and the resources consumed around 7.5% LookUp Table, 1.9% Flip-Flops, and 5.8% of the memory. EFPP was also more area efficient. According to our results, EFPP would reduce the area and volume of hardware compared to other peer works.     

A Novel RBF Neural Network Training Methodology to Predict Toxicity to Vibrio Fischeri

Summary This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischerifor a heterogeneous set of compounds.     

Adaptive population-based multi-objective optimization in SDN controllers for cost optimization

In Wireless Sensor Networks, Software Defined Networks (SDN) provide a logically centralized control plane as a potential means of streamlining network management (WSNs). The employment of several SDN controllers to build a physically distributed SDN is a common tactic to boost speed, expand scalability, and offer fault tolerance. However, the deployment of many controllers results in increased synchronization and deployment expenses. Therefore, selecting the optimal location for SDN controllers to improve WSN performance is a research issue. In this paper, the multi-objective optimization problem known as the controller placement problem (CPP) is initially formulated. Cost, time, and reliability are just a few of the restraints that are taken into consideration in this regard. In addition, a new Adaptive Population-Based Cuckoo Optimization (APB-CO) for optimal controller placement is implemented. In the end, APB-CO performs experiments to validate the efficacy by analyzing Sync (7.5), Coverage (47), Controller Cost (4.8), and Fitness (0.6983) for the 100th node variation at network 1. The proposed model obtained the controller cost as 34.4, compared to the existing method such as Simulated Annealing (44.3) and Greedy Approach (42.6).     

Traffic flow monitoring in software-defined network using modified recursive learning

The recursive network design used in this paper to monitor traffic flow ensures accurate anomaly identification. The suggested method enhances the effectiveness of cyber attacks in SDN. The suggested model achieves a remarkable attack detection performance in the case of distributed denial-of-service (DDoS) attacks by preventing network forwarding performance degradation. The suggested methodology is designed to teach users how to match traffic flows in ways that increase granularity while proactively protecting the SDN data plane from overload. The application of a learnt traffic flow matching control policy makes it possible to obtain the best traffic data for detecting abnormalities obtained during runtime, improving the performance of cyber-attack detection. The accuracy of the suggested model is superior to the MMOS, FMS, DATA, Q-DATA, and DEEP-MC by 19.23%, 16.25%, 47.61%, 16.25%, and 12.04%.     

First- and second-order Raman scattering from MoTe<Subscript>2</Subscript> single crystal

We report on Raman experiments performed on a MoTe₂ single crystal. The system belongs to the wide family of transition metal dichalcogenides which includes several of the most interesting two-dimensional materials for both basic and applied physics. Measurements were performed in the standard basal plane configuration, by placing the ab plane of the crystal perpendicular to the wave vector k _i of the incident beam to explore the in-plane vibrational modes, and in the edge plane configuration with k _i perpendicular to the crystal c axis, thus mainly exciting out-of-plane modes. For both configurations we performed a polarization-dependent study of the first-order Raman components and detailed computation of the corresponding selection rules. We were thus able to provide a complete assignment of the observed first-order Raman peaks, in agreement with previous literature results. A thorough analysis of the second-order Raman bands, as observed in both basal and edge plane configurations, provides new information and allows a precise assignment of these spectral structures. In particular, we have observed and assigned Raman active modes of the M point of the Brillouin zone previously predicted by ab initio calculations but never previously measured.     

QoS-aware resource allocation and fault tolerant operation in hybrid SDN using stochastic network calculus

Mobile edge computing (MEC) is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the users. With the advent of the next-generation mobile networks, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. So, the integration of software defined networking (SDN) and MEC techniques was seriously considered to facilitate the orchestration and management of Mobile Edge Hosts (MEH). Edge clouds can be installed as an interface between the local servers and the core to provide the required services based on the known concept of the SDN networks. Nonetheless, the problem of reliability and fault tolerance will be of great importance in such networks. The paper introduced a dynamic architecture that focuses on the end-to-end mobility support required to maintain service continuity and quality of service. This paper also presents an SDN control plane with stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Compared to other solutions investigated in the literature, the proposed approach exhibits a significant increase in the throughput distributed over the active links of mobile edge hosts. It also proved that the outage index and the system’s aggregate data rate can be effectively improved by up to 32%.     

Blocking probability analysis of pruned optical banyan networks on vertical stacking scheme with extra banyan planes

Vertically stacked pruned optical banyan networks with extra planes (in short, EP-VSOB networks) have lower switch count and optimal time complexity (O(log₂ N)) for routing N input requests. However, blocking probability is relatively higher than that of a VSOB networks using regular banyan planes. In the EP-VSOB architecture, the number of pruned planes has always been considered as , and a few extra planes (regular banyan) have been added with these pruned planes. In this paper we present the results of blocking analysis of a more generalized architecture in which the number of pruned planes can be 2^x, where x ? 0 in addition to the variable extra planes. This generalization helps us make a compromise between different constraints and performance metrics. Our simulation results show that for some given performance requirements (e.g. cost, speed or blocking probability) we can choose a network that has lower switch count compared to -plane pruned crosstalk-free optical banyan networks. For example, to ensure blocking probability <0.02, previously we would chose a pruned network of 32 pruned planes and 1 extra planes (a regular banyan); however, our simulations results show that a network of 16 pruned planes and 2 extra planes is enough to ensure the same performance. It is notable that, the hardware cost decreases by 28.65% in this new combination of pruned and extra planes. We believe our results will provide more flexibility in choosing a particular EP-VSOB network satisfying given requirements.     

Systemic risk on different interbank network topologies

In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents’ performance. By changing the agent’s trust on its neighbor’s performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents’ heterogeneity.     

Design and performance evaluation of a metro WDM storage area network with IP datagram support

Storage area networks (SANs) are becoming an important part of optical MANs (metropolitan area networks). Growing storage and business-continuity needs; high-bandwidth, low latency requirements for SANs; storage infrastructure consolidation; and post-9/11 regulatory issues are among the several driving factors to push this trend. We, in this paper, consider a metro wavelength division multiplexing (WDM) SAN that allows the transmission of variable packet size such as Internet protocol (IP) datagram and evaluate its performance by means of discrete-event simulation. The network is based on one fixed transmitter and multiple fixed receivers. Beginning with an introduction and the context of this work, we describe the network and node architectures; and introduce the medium access control (MAC) protocols. Subsequently, using the Poisson and self-similar traffic, we present and discuss performance of the proposed network architecture in terms of throughput and queuing delay under symmetric and asymmetric traffic scenarios. The simulation results suggested that the proposed architecture is suitable for SAN applications which demand low queuing delay and high throughput.