Link Mapping Research Articles

A Reliable Interference-Aware Mapping Algorithm for Airborne Tactical Network Virtualization

Airborne tactical networks (ATNs) is driving the growing development of network-centric warfare by maintaining coverage and providing reach-back to military units. However, the key function of ATNs is impeded by the network ossification that is deep-rooted in the tightly coupled, vertically integrated architecture of traditional ATNs. Network virtualization (NV) can provide a more flexible and scalable ATN architecture as a solution, breaking the tight coupling between applications and network infrastructure. One important aspect of NV is virtual network embedding (VNE), which instantiates multiple virtual networks on a shared substrate network. However, existing efforts are not necessarily optimal for the virtualization of ATNs due to the absence of QoS-compliant capacity for the complex interference in air-combat field. To tackle this difficulty, a reliable interference-aware VNE algorithm, termed VNE-RIA, is proposed to provide reliable guarantee in the coordinated node and link mapping for various airborne tactical virtual networks (ATVNs). In the node mapping, the VNE-RIA adopts a novel node ranking approach to rank all substrate and virtual nodes, considering the complex interference including link interference, environmental noise and malicious attacks. In the link mapping, an improved anypath link mapping approach, based on the anypath routing scheme, is adopted to improve the reliability and efficiency of mapping virtual links by exploiting the unique features of wireless channels and the influence of different transmission rates. Numerical simulation results reveal that VNE-RIA algorithm outperforms typical and latest heuristic wireless VNE algorithms under the complex electromagnetic interference of ATNs.

Promising Modern Research Areas in Smart Mobility

Contemporary smart mobility technology makes it possible to solve the problems of urban transport, improving quality of urban mobility, increasing availability of transport, and ensuring its efficiency. The objective of the study is to identify key trends of research on smart mobility in recent scientific sources. The author, using VOS Viewer program, carried out a bibliometric analysis of publications indexed in the Web of Science and Scopus databases and containing keywords related to smart urban mobility. Co-citations, cross-references, shared keywords, co-authorships have been analyzed for the entire set of selected publications. As a result, a map of links between publications in the field of smart mobility was built, defining existing relationships between keywords and authors in this area. Also, a map of clusters was built , which displays proximity of publications to each other. A cluster comprises a group of research closely related via research topics, authors, and keywords. Clustering of publications was conducted followed by identifying six clusters, each of which reflects single promising research area in the field of smart mobility. The results obtained make it possible to identify promising development trends for smart mobility through automated processing of a highquality array of publications. The results can be used by municipal officials, transportation business and academia to review and analyze emerging smart mobility technologies and to make informed urban transport planning decisions.

Distributed parallel genetic algorithm for online virtual network embedding

SummaryNetwork virtualization (NV) has emerged as a promising paradigm to address the constraints of implementing new protocols and services in existing network architecture by allowing the simultaneous coexistence of multiple heterogeneous virtual networks on a shared substrate infrastructure. Hence, NV is a critical technology for establishing future network architectures (e.g., 5G network and the smart Internet of Things [IoT]). Virtual network embedding (VNE) is a major challenge in NV since it is acknowledged as ‐hard. Many VNE solutions have been proposed over the past decade. However, the proposed solutions merely centralize VNE node mapping while recommending virtual link embedding for the shortest path method or multicommodity flow (MCF) mechanism. This research paper presents an intelligent virtual network orchestration based on genetic algorithm (GA) for the link mapping stage that implements distributed parallelism to significantly and efficiently reduce the operation time. Our extensive simulations have demonstrated that the proposed algorithm not only outperforms the state‐of‐the‐art VNE algorithm in all performance metrics but also achieves 44.01% faster embedding speed than the most well‐known, fastest link mapping method in VNE.

Mobile Communications-Enabled Smart Grid Cosimulator System Design

In this article, we present a system design approach for a simulator constructed in MATLAB/Simulink used to simulate power systems and the underlying communication system, i.e., device-to-device (D2D) communication protocol stack of long-term evolution (LTE). Emphasis is placed on the characterization of the continuous time-discrete event (CT-DE) cosimulation interface used to develop this simulator. We model the mapping of communication links between D2D nodes as a bipartite digraph, and proceed to characterize the dynamics of information exchange between power system signals and LTE nodes using the discrete event specification formalism. The joint CT-DE dynamics were tested with a simple fault location, isolation, and restoration application. The results indicated that the control signal shifts in the power system simulator were synchronized with the appropriate LTE messaging dynamics.

Modeling on virtual network embedding using reinforcement learning

SummaryIt is well known that virtual network (VN) embedding (VNE) aims to solve how to efficiently allocate physical resources to a VN. However, this issue has been proved to be an NP‐hard problem. Besides, as most of the existing approaches are based on heuristic algorithms, which is easy to fall into local optimal. To address the challenge, we formalize the problem as a mixed integer programming problem and propose a novel VNE method based on reinforcement learning in this article. And to solve the problem, we introduce a pointer network to generate virtual node mapping strategies through an attention mechanism, and design a reward function related to link resource consumption to build the connection between node mapping and link mapping stages of VNE. In addition, we present a policy gradient optimization mechanism to leverage the reward information obtained from the sampled solutions, and design an active search based process to automatically update the parameters of the neural network and to obtain near‐optimal embedding solution. The experimental results show that the proposed method can improve the performance in average physical node utilization and long‐term revenue to cost ratio comparing than that of the existing models.

Emerging intelligent big data analytics for cloud and edge computing

Emerging intelligent big data analytics for cloud and edge computing

DSCD: Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm

In the coming 5G era, more and more applications are time-delay sensitive, and the importance of low delay in fields such as telemedicine, autonomous driving and military order transmission is self-evident. However, for different application scenarios, their delay requirements are different. How to meet different Quality of service (QoS) requirements simultaneously becomes a challenge. The emergence of network virtualization (NV) technology can overcome this problem. However, it is difficult for the existing embedding algorithms to meet the requirements of virtual networks on time delay performance. Therefore, in order to study the delay problem of virtual network, we proposed a Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm (DSCD-VNE) in this paper. First of all, different from the traditional virtual network embedding (VNE) algorithm, this algorithm divides the mapping process into a “node-link-node” three-stage embedding algorithm. Second, node resource metrics and set constraints are introduced in the generation phase of candidate link matrix. Third, the algorithm takes candidate link matrix as the core, and uses Kruskal minimum spanning tree to choose the link with the shortest delay for embedding. Fourth, path splitting mechanism and $K$ -shortest path algorithm are introduced with the aim of increasing the utilization of the substrate links in link mapping stage. The simulation results show that the proposed DSCD-VNE algorithm greatly improves the performance of the algorithm compared with the traditional node-link two-stage algorithm, especially in the aspect of delay, the improvement rate is as high as 77%.

A Constructive Particle Swarm Optimizer for Virtual Network Embedding

Virtual network embedding (VNE) is a key technique for flexible sharing of physical resources in the modern Internet. As the VNE problem is nondeterministic polynomial hard, natural inspired population-based algorithms have been increasingly considered as promising approaches. But as the VNE problem has two tightly related tasks: node mapping and link mapping, it remains challenging for metaheuristics to deal with both tasks coordinately and effectively. This paper proposes a constructive particle swarm optimizer for virtual network embedding (CPSO-VNE). CPSO-VNE uses discrete vectors and matrices with probabilities to encode positions and velocities on VNE search space, respectively. What makes CPSO-VNE different from previous works is the step-by-step solution construction scheme introduced in CPSO-VNE. In this scheme, each node is mapped along with the mapping of its adjacent virtual links. In this way, node and link mappings are coordinated in one stage. In addition, with this step-by-step construction scheme, path information of networks can be introduced as heuristic information to guide the search, which can further improve performance. The proposed method is tested on the scenarios with a single VN request and with a set of online VN requests. The simulation results show that the proposed approach is promising.

A single-player Monte Carlo tree search method combined with node importance for virtual network embedding

As a critical technology in network virtualization, virtual network embedding (VNE) focuses on how to allocate physical resources to virtual network requests efficiently. Because the VNE problem is NP-hard, most of the existing approaches are heuristic-based algorithms that tend to converge to a local optimal solution and have a low performance. In this paper, we propose an algorithm that combines the basic Monte Carlo tree search (MCTS) method with node importance to apply domain-specific knowledge. For a virtual network request, we first model the embedding process as a finite Markov decision process (MDP), where each virtual node is embedded in one state in the order of node importance that we design. The shortest-path algorithm is then applied to embed links in the terminal state and return the cost as a part of the reward. Due to the reward delay mechanism of the MDP, the result of link mapping can affect the action selected in the previous node mapping stage, coordinating the two embedding stages. With node importance, domain-specific knowledge can be used in the Expansion and Simulation stages of MCTS to speed up the search and estimate the simulation value more accurately. The experimental results show that, compared with the existing classic algorithms, our proposed algorithm can improve the performance of VNE in terms of the average physical node utilization ratio, acceptance ratio, and long-term revenue to cost ratio.

Multicriteria dragonfly graph theory based resource optimized virtual network mapping technique for home medical care service provisioning in cloud

The cloud offers more services across multiple infrastructures and rapidly growing areas of development in medical care. In the cloud, Network virtualization allows multiple isolated virtual networks (VNs) for flexible sharing of network resources. The virtual network mapping in Network virtualization provides the dynamic virtual node and link resources to satisfy the user needs. The major challenges of cloud computing are optimally and resourcefully responds to each user service requests with minimum time. To address these problems in distributed and hybrid cloud environments, Multicriteria Dragonfly based Graph Theory Resource Optimized Virtual Network Mapping (MD-GTROVNM) technique is introduced. The main objective of the MD-GTROVNM technique is to improve the efficiency of virtual network request mapping with less resource utilization. In the MD-GTROVNM technique, Multicriteria Dragonfly based Graph Theory performs both virtual node mapping and link mapping with reasonable resource utilization such as CPU, memory, and bandwidth. In node mapping, the Multicriteria Dragonfly optimization technique is applied to find the optimal physical node among the population that satisfies the resource constraints. The proposed Multicriteria Dragonfly optimization algorithm achieved a more optimal solution for virtual network mapping. The experimental scenario is carried out with various parameters such as mapping efficiency, computation time, Request acceptance ratio and memory consumption with a number of VN requests. The observed results confirm that the MD-GTROVNM technique effectively increases the mapping efficiency, Request acceptance ratio and minimizes the computation time as well as memory consumption when compared to the existing techniques.

Breakpoint Mapping of Symptomatic Balanced Translocations Links the EPHA6, KLF13 and UBR3 Genes to Novel Disease Phenotype.

De novo balanced chromosomal aberrations (BCAs), such as reciprocal translocations and inversions, are genomic aberrations that, in approximately 25% of cases, affect the human phenotype. Delineation of the exact structure of BCAs may provide a precise diagnosis and/or point to new disease loci. We report on six patients with de novo balanced chromosomal translocations (BCTs) and one patient with a de novo inversion, in whom we mapped breakpoints to a resolution of 1 bp, using shallow whole-genome mate pair sequencing. In all seven cases, a disruption of at least one gene was found. In two patients, the phenotypic impact of the disrupted genes is well known (NFIA, ATP7A). In five patients, the aberration damaged genes: PARD3, EPHA6, KLF13, STK24, UBR3, MLLT10 and TLE3, whose influence on the human phenotype is poorly understood. In particular, our results suggest novel candidate genes for retinal degeneration with anophthalmia (EPHA6), developmental delay with speech impairment (KLF13), and developmental delay with brain dysembryoplastic neuroepithelial tumor (UBR3). In conclusion, identification of the exact structure of symptomatic BCTs using next generation sequencing is a viable method for both diagnosis and finding novel disease candidate genes in humans.

Multi-domain virtual network embedding with dynamic flow migration in software-defined networks

Software Defined Networking (SDN) decouples the control and data planes of a network, and employs a central controller to provide effective use of network resources and ease of service provision. Virtualization in SDN empowers the virtual SDN networks to share common physical network infrastructures, and, thus, make them capable for versatile service provisioning. In such setting, SDN hypervisor supports multiple virtual SDN network (vSDN) in which each vSDN has its own controller. To create virtual networks and achieving optimal sharing of physical network resources, virtual network embedding (VNE) algorithms are designed. Single domain VNE is a well-studied problem in NV literature. However, in most of the real life scenarios, VNs are provisioned across heterogeneous administrative domains belonging to different Infrastructure Providers (InPs).In this paper, we propose a VNE algorithm, namely vSDN-CLA, using Irregular Cellular Learning Automata (ICLA) for multi-domain SDN networks. We consider two aspects – optimal mapping of virtual nodes and links into multi-domain substrate network, and optimal placement of SDN controller with respect to throughput and end-to-end delay. We extend the vSDN-CLA by considering dynamic flow migration to achieve resource optimal routing. We evaluate the proposed schemes using Mininet. We observe that the proposed schemes outperform the existing benchmark schemes with respect to throughput, end-to-end delay, and virtual network request acceptance ratio under both the single and multi-domain environments.

On the methodological limits of the commodity form theory of law in The Corporation, Law and Capitalism

Grietje Baars’s The Corporation, Law and Capitalism is an impressive and ambitious historical unfolding of the corporate legal form starting in Northern Europe in the early modern period and expanding globally in the 20th and 21st centuries.1 Methodologically, it is centred on the abstracting of a specific institution—the corporation—as the key analytic for exploring the historical relation between law and capitalism. Baars argues the corporation is one of the most important structural mechanisms at the heart of this relationship. In effect, the corporation enables a tracing and mapping of the various subterranean links between institutions and actors of capitalism, imperialism, and law. In effect, the central institutions and actors Baars chooses to illustrate the ways in which capitalism, imperialism, and law are tied together belong to the domain of International Criminal Law (ICL). Tracing developments from Nuremberg and Tokyo to the ICTY, Rwanda, the ICC and, finally, the more recent development of Corporate Social Responsibility, Baars’s analysis shakes up the dichotomies created by these historical processes. In other words, the structural persistence of the capitalist corporation since the 17th century explains why ICL and the liberal ideal that accompanies institutions of the international legal order is made to fail, or why it cannot fulfil its promise of bringing anyone to justice regardless of who they are. Instead, the corporation reveals a world where the legal form distinguishes between those who can avoid justice and responsibility, and those who do not have the means to contest that structural impunity.

Congestion-Aware and Energy-Aware Virtual Network Embedding

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.

Early-Warning-Time-Based Virtual Network Live Evacuation Against Disaster Threats

Virtualization facilitates heterogeneous cloud applications to share the same physical infrastructure with admirable flexibility, while resource efficiency and survivability are critical concerns for virtual network embedding (VNE). As more and more internet applications migrate to the cloud, the resource efficiency and the survivability of VNs, such as single link failure or large-scale disaster survivability, have become crucial issues. Separating the VNE problem into node and link mapping sub-problems without coordination might cause a high embedding cost. This dissertation presents two independent approaches to solve the aforementioned challenges. First, we study two-stage coordinated survivable VNE (SVNE) problem and propose an adaptive path splitting based SVNE (APSS) scheme. We first develop a concise anchor node strategy to restrict the solution space of the candidate substrate nodes, which coordinates node mapping with link mapping to limit the distance spans of the virtual links. Then, we employ an adaptive path splitting policy to provide full protection against single-link failures with partial backup resource, and design an agile frequency slot windows choosing mechanism to mitigate the spectrum fragmentation for link resource efficiency. Simulation results demonstrate that the proposed APSS scheme can achieve satisfactory performance in terms of spectrum utilization and blocking ratio. Second, we propose a synchronous evacuation strategy for VNs with dual virtual machines (VMs) inside a disaster risk zone (DRZ), which suffer higher risks than the VNs with single. The evacuation strategy exploits post-copy technique to sustain the online service alive and enhances synchronous VM migrations to shorten the dual-VM evacuation time. Numerical results show that the proposed strategy can outperform the best-effort scheme in terms of average and total evacuation times of dual-VMs.

An Efficient and Fast Embedding Algorithm for the Virtual Networks

Virtual network mapping is a hot issue over the past decade, which aims to map virtual networks to the underlying network as required. This procedure is a NP-hard question, therefore many previous algorithms just impose some constraints or use simple heuristic method to get the relative optimal result. Traditional researches almost focus on two independent stages: node embedding and link mapping. To address existing questions such as low acceptance ratio and high mapping time, we propose an efficient and fast embedding algorithm, named as AEF(An Efficient and Fast embedding algorithm), which is a two coordinated mapping stages algorithm. AEF uses some innovative strategies in two stages to improve the mapping time and ensure high performance. It has great advantages in large-scale complex networks or occasions with high real-time requirements because of faster mapping speed. Besides, it will also have more practical value in actual scenarios due to higher acceptance ratio and more balanced link load. The worst time complexity of our AEF is proved to be O(|N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> | <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> · |N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</sup> |·(log(|N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> |) + |N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</sup> |)). A large number of experimental results show that our algorithm is faster than most other algorithms and ensures the overall better efficiency.

P4-MACsec: Dynamic Topology Monitoring and Data Layer Protection With MACsec in P4-Based SDN

We propose P4-MACsec to protect network links between P4 switches through automated deployment of MACsec, a widespread IEEE standard for securing Layer 2 infrastructures. It is supported by switches and routers from major manufacturers and has only little performance limitations compared to VPN technologies such as IPsec. P4-MACsec introduces a data plane implementation of MACsec including AES-GCM encryption and decryption directly on P4 switches. P4-MACsec features a two-tier control plane structure where local controllers running on the P4 switches interact with a central controller. We propose a novel secure link discovery mechanism that leverages protected LLDP frames and the two-tier control plane structure for secure and efficient management of a global link map. Automated deployment of MACsec creates secure channel, generates keying material, and configures the P4 switches for each detected link between two P4 switches. It detects link changes and performs rekeying to provide a secure, configuration-free operation of MACsec. In this paper, we review the technological background of P4-MACsec and explain its architecture. To demonstrate the feasibility of P4-MACsec, we implement it on the BMv2 P4 software switch and validate the prototype through experiments. We evaluate its performance through experiments that focus on TCP throughput and round-trip time. We publish the prototype and experiment setups on Github.

An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network

As one of key technologies of the fifth-generation (5G) communication system, network slicing can share the underlying infrastructure with different application requirements and ensure that the slices can be isolated from each other. This paper proposes an end-to-end (E2E) network slicing resource allocation algorithm based on Deep Q-Networks (DQN), which is suitable for multi-slice and multi-service scenarios. This algorithm jointly considers the radio access network slices and core network slices to dynamically allocate resources to maximize the number of access users. First we build such a model, which is a mixed integer programming problem and it needs to be dynamically adjusted according to the changes of environment. We propose to use DQN algorithm to solve this problem, which can perceive changes in the environment and make dynamic decisions. Under each decision, we need to calculate the reward value of DQN, so we divide the problem into the core side and the access side. Then the dynamic knapsack algorithm and the link mapping algorithm are used to obtain the reward. The simulation results show that the average access rate of DQN scheme is higher than 97%. Compared with the optimal allocation scheme of access side, the average access rate is increased by 9% for delay constrained slices and 5% for rate constrained slices in a dynamic environment.

Explore Deep Neural Network and Reinforcement Learning to Large-scale Tasks Processing in Big Data

Large-scale tasks processing based on cloud computing has become crucial to big data analysis and disposal in recent years. Most previous work, generally, utilize the conventional methods and architectures for general scale tasks to achieve tons of tasks disposing, which is limited by the issues of computing capability, data transmission, etc. Based on this argument, a fat-tree structure-based approach called LTDR (Large-scale Tasks processing using Deep network model and Reinforcement learning) has been proposed in this work. Aiming at exploring the optimal task allocation scheme, a virtual network mapping algorithm based on deep convolutional neural network and [Formula: see text]-learning is presented herein. After feature extraction, we design and implement a policy network to make node mapping decisions. The link mapping scheme can be attained by the designed distributed value-function based reinforcement learning model. Eventually, tasks are allocated onto proper physical nodes and processed efficiently. Experimental results show that LTDR can significantly improve the utilization of physical resources and long-term revenue while satisfying task requirements in big data.

Pre-service Physics Teachers Perception about Concept Map, Mind map, and Link Map for Innovative Learning

This study aims to describe students’ perceptions of the characteristics of assisted learning mapping methods such as concept maps (CM), mind maps (MM), and link maps (LM). The study was conducted with qualitative and quantitative data collection with a sample of 10 students as pre-service physics teachers. Quantitative data becomes a supporting part of the qualitative description. Each student was asked to make a concept map, mind map, and link maps with physics material. Furthermore, students’ performance is analyzed in the description form of the differences and effectiveness of the mapping when applied in physics learning. These results indicated that mapping has a positive effect on the development of students’ thinking abilities, especially in identifying the right concepts for physics material.