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.     

Virtualized Networks and Virtualized Optical Line Terminal (vOLT)

The success of the Internet and the proliferation of the Internet of Things (IoT) devices is forcing telecommunications carriers to re-architecture a central office as a datacenter (CORD) so as to bring the datacenter economics and cloud agility to a central office (CO). The Open Network Operating System (ONOS) is the first open-source software-defined network (SDN) operating system which is capable of managing and controlling network, computing, and storage resources to support CORD infrastructure and network virtualization. The virtualized Optical Line Termination (vOLT) is one of the key components in such virtualized networks.     

A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios

Traffic congestion has been an actual problem in large cities, causing personal inconvenience and environmental pollution. To solve this problem, new applications for Intelligent Transportation System (ITS) have been created, to monitor actual traffic conditions. Therefore, fast, reliable and safe systems are desirable for creating a real intelligent transportation environment. Deep learning algorithms have been proposed for a better understanding of traffic behavior from a security-related perspective. Thus, we aim to maximize the safety problems using a deep learning algorithm, where a novel policy gradient model is presented for detecting vehicular misuse. The proposed model uses a triple network replay algorithm, maximizing the network convergence speed. Three networks are selected to optimize the policy network variables. Finally, the replay algorithm is partitioned with the aim of obtaining a faster model. Simulations on a real urban map are performed in a scenario with the integration of 5G or 6G networks. An architectural model for the integration of a Vehicular Ad-hoc Network (VANET) and cellular networks is determined in software-defined networking (SDN). The results show that the accuracy prediction of the proposed system presents better performance compared to related studies, where the proposed model increases its convergence speed and cumulative reward. Thus, the ITS improvement by the proposed deep learning algorithm increases the prediction accuracy, and reduces the transmission delay, treating the traffic path according to the congestion.     

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%.     

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.     

All-optical add-drop node for optical packet-switched networks

We have demonstrated all-optical packet add-drop for all-optical packet-switched networks. Intelligent all-optical add-drop of packets is performed, based on all-optical processing of packet headers. The header and payload rates are 5 and 10 Gbits/s, respectively.     

A Fault-Tolerant Transmission Scheme in SDN-Based Industrial IoT (IIoT) over Fiber-Wireless Networks

Driven by the emerging mission-critical and data-intensive applications in industrial intelligent manufacturing, the software-defined network (SDN) based fiber-wireless access network (FiWi) is attracting considerable attention thanks to its capability of central control and large bandwidth. However, the heterogeneity of the network leads to new challenges, since the packet loss can be caused either by the poor channel quality of wireless links or network component failures. A novel and adaptive mechanism combining sparse random linear network coding with parallel transmission (SNC-PT) is proposed to achieve the fault-tolerance against high packet loss rate and any network element malfunction. We illustrate the benefits of using the SNC-PT mechanism to improve fault tolerance by characterizing the network performance with respect to the completion time and goodput along with its relationship to channel quality and node failures. We show that significant performance gains can be obtained in comparison with conventional uncoded transmission based on transmission control protocol (TCP). The simulation results show that the SNC-PT mechanism is fault-tolerant, while it can significantly shorten the data transmission completion time to at least 12% of the baseline and increase the goodput by about 10% compared to other coding schemes such as random linear network coding.     

Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.     

A resource-saving decoding scheme of the orthogonally concatenated codes for optical transport networks in FPGA

A decoding scheme of the orthogonally concatenated codes with low resource utilizations is proposed. In the optical transport networks (OTN), forward error-correction (FEC) techniques are used to reduce the errors which occur in transmissions. Two-orthogonal-concatenated (TOC) codes are widely used in FEC techniques for their powerful error-correction capabilities based on the iterative decoding procedure. However, the framing structure is complex so the decoding procedure is more difficult than the decoding of in–out concatenated codes. And the powerful error-correction capability relies on the multi-iterative decoding processing, thus how to effectively utilize the hardware resources is a very important problem. Especially when the decoding procedure is implemented in the field programmable gate array (FPGA) devices, effective optimizations are required for the limited resources. In this paper we present an iterative decoding scheme in FPGA with low resource utilizations. As an example, an actual engineering application under the G.975.1 recommendation is given to show the efficiency of the proposed design.     

Novel memory efficient LDPC decoders for beyond 5G

Beyond-5G wireless networks are expected to gain a excellent trade-off among computational accuracy, latency, and efficient use of available resources. This poses a significant challenge to the channel decoder. In this paper, a novel memory efficient algorithm for decoding Low-Density Parity-Check (LDPC) codes is proposed with a view to reduce the implementation complexity and hardware resources. The algorithm, called Check Node Self-Update (CNSU) algorithm, is based on layered normalized min-sum (LNMS) decoding algorithm while utilizing iteration parallel techniques to integrate both Variable Nodes (VNs) message and A-Posterior Probability(APP) message into the Check Nodes (CNs) message, which eliminates memories of both the VNs message and the APP message as well as updating module of APP message in CNs unit. Based on the proposed CNSU algorithm, design of partially parallel decoder architecture and serial simulations followed by implementation on the Stratix II EP2S180 FPGA are presented. The results show that the proposed algorithm and implementation bring a significant gain in efficient using of available resources, include reducing hardware memory resources and chip area while keeping the benefit of bit-error-rate (BER) performance and speeding up of convergence with LNMS, which are beneficial to apply in Beyond-5G wireless networks.     

SELF-ORGANIZED CRITICALITY IN ONE-DIMENSIONAL PACKET FLOW MODEL

Packet flow affects the behavior of Internet routers, which in return regulates the flow. Even a non-correlated uniform packet flow from a terminal will be modulated to show correlated fluctuations by going through the network nodes. In this paper, we study a simple model in an abstract level to describe intuitively the self-organized criticality in packet level, the emergence of collective behavior of packets, which causes the long-range dependence of congestion in computer networks. We find that the character of the jam lifetime is consistent with the measurement results, the packet delivery time appears the feature of 1/f noise, and the intervals between the packet arrivals are power-law distributed.     

A survey on 5G: The next generation of mobile communication

The rapidly increasing number of mobile devices, voluminous data, and higher data rate are pushing to rethink the current generation of the cellular mobile communication. The next or fifth generation (5G) cellular networks are expected to meet high-end requirements. The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and very high-speed data transfer. The 5G networks would provide novel architectures and technologies beyond state-of-the-art architectures and technologies. In this paper, our intent is to find an answer to the question: “what will be done by 5G and how?” We investigate and discuss serious limitations of the fourth generation (4G) cellular networks and corresponding new features of 5G networks. We identify challenges in 5G networks, new technologies for 5G networks, and present a comparative study of the proposed architectures that can be categorized on the basis of energy-efficiency, network hierarchy, and network types. Interestingly, the implementation issues, e.g., interference, QoS, handoff, security–privacy, channel access, and load balancing, hugely effect the realization of 5G networks. Furthermore, our illustrations highlight the feasibility of these models through an evaluation of existing real-experiments and testbeds.     

Major impact of queue-rule choice on the performance of dynamic networks with limited buffer size

We investigate the similarities and differences among three queue rules, the first-in-first-out(FIFO) rule, last-in-firstout(LIFO) rule and random-in-random-out(RIRO) rule, on dynamical networks with limited buffer size. In our network model, nodes move at each time step. Packets are transmitted by an adaptive routing strategy, combining Euclidean distance and node load by a tunable parameter. Because of this routing strategy, at the initial stage of increasing buffer size, the network density will increase, and the packet loss rate will decrease. Packet loss and traffic congestion occur by these three rules, but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks. If packets are lost and traffic congestion occurs, different dynamic characteristics are shown by these three queue rules. Moreover, a phenomenon similar to Braess' paradox is also found by the LIFO rule and the RIRO rule.     

Hybrid OCS/OBS interconnect in intra-data-center network

We report on a data center network(DCN) architecture based on hybrid optical circuit switching(OCS) and optical burst switching(OBS) interconnect for dynamic DCN connectivity provisioning. With the combination of the centralized and distributed control of the software-defined optical networks, the proposed interconnect can achieve unprecedented flexibility in dealing with both mice and elephant flow in the DCN. Numerical simulation is employed to investigate the performance of the proposed architecture. The results show that the OBS module has preferable performance in dealing with a larger burst packet, and the throughput is constrained by the capacity of the server random access memory module.     

On coding for reliable communication over packet networks

We consider the use of random linear network coding in lossy packet networks. In particular, we consider the following simple strategy: nodes store the packets that they receive and, whenever they have a transmission opportunity, they send out coded packets formed from random linear combinations of stored packets. In such a strategy, intermediate nodes perform additional coding yet do not decode nor wait for a block of packets before sending out coded packets. Moreover, all coding and decoding operations have polynomial complexity.We show that, provided packet headers can be used to carry an amount of side-information that grows arbitrarily large (but independently of payload size), random linear network coding achieves packet-level capacity for both single unicast and single multicast connections and for both wireline and wireless networks. This result holds as long as packets received on links arrive according to processes that have average rates. Thus packet losses on links may exhibit correlations in time or with losses on other links. In the special case of Poisson traffic with i.i.d. losses, we give error exponents that quantify the rate of decay of the probability of error with coding delay. Our analysis of random linear network coding shows not only that it achieves packet-level capacity, but also that the propagation of packets carrying “innovative” information follows the propagation of jobs through a queueing network, thus implying that fluid flow models yield good approximations.     

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。     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

Metro Optical Networks for Homeland Security

Metro optical networks provide an enticing opportunity for strengthening homeland security. Many existing and emerging fiber-optic networks can be adapted for enhanced security applications. Applications include airports, theme parks, sports venues, and border surveillance systems. Here real-time high-quality video and captured images can be collected, transported, processed, and stored for security applications. Video and data collection are important also at correctional facilities, courts, infrastructure (e.g., dams, bridges, railroads, reservoirs, power stations), and at military and other government locations. The scaling of DWDM-based networks allows vast amounts of data to be collected and transported including biometric features of individuals at security check points. Here applications will be discussed along with potential solutions and challenges. Examples of solutions to these problems are given. This includes a discussion of metropolitan aggregation platforms for voice, video, and data that are SONET compliant for use in SONET networks and the use of DWDM technology for scaling and transporting a variety of protocols. Element management software allows not only network status monitoring, but also provides optimized allocation of network resources through the use of optical switches or electrical cross connects.     

High-performance networks for broadband multimedia services

Research and development in Advanced Communications technologies for Europe (RACE) played a key role in developing the crucial technology of Asynchronous Transfer Mode (ATM), which can provide a high-performance networking platform for broadband multimedia communications while allowing access to the existing worlds of voice, data, and broadcasting. The functionalities of high-performance networks in the user plane, control plane, and management planes to provide integral performance for the satisfaction of the users have been tested in accompanying measures such as the TEN-IBC projects of the European Commission. The European Union (EU) is now starting Advanced Communications Technologies and Services (ACTS)—the follow-up to RACE—as one of the major specific programs in the fourth research framework, involving 630 million ECUs of European Union contribution over four years. High-performance networking is a key development area within ACTS. It has a clear objective of enabling the deployment of a coherent and comprehensive trans-European information highway consisting of a terrestrial (optical), mobile, and satellite network infrastructure supporting broadband multimedia communications with maximum flexibility and a cost-effective evolution approach. Operational trials of high-performance networks involving different types of users, applications, network operators (national hosts), and the European ATM pilot network are planned as a part of the new ACTS projects. This article summarizes the projects involved and their activities toward the objectives of provisioning the high-performance network information highway for broadband multimedia applications.