Link Mapping Research Articles

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Knowledge mapping of links between dendritic cells and allergic diseases: A bibliometric analysis (2004–2023)

In this study, bibliometric analysis was carried out to comprehend the global research trends, hotspots, scientific frontiers, and output characteristics of the links between dendritic cells (DCs) and allergic diseases from 2004 to 2023. Publications and their recorded information were retrieved from the Web of Science Core Collection (WoSCC). VOSviewer and Citespace were used to visualize the hotspots and trends of research area. ChemBio 3D, Autodock tools, and Discovery Studio were used to visualize the molecular docking results of hotspots. A total of 4861 articles were retrieved. The number of publications (Np) was in a high and stable state. Years 2011 and 2017 were two peaks in Np. The largest contributor in terms of publications, scholars, and affiliations was the USA. The paper published in NATURE MEDICINE (IF: 82.9) and written by Trompette, A in 2006 had the highest global citation score (GCS). Keywords, such as “asthma,” “t-cells,” “inflammation,” “expression,” “atopic dermatitis,” “food allergy,” “gut microbiota,” “murine model,” and “cytokines related to immunity” appeared the most frequently. Most of the binding free energy of the key active components of Saposhnikovia divaricata docked with toll-like receptor proteins well. This bibliometric study aimed to help better comprehend the present state and make decisions from a macro viewpoint.

THE EVOLUTION OF ECOTOURISM ON GEOHERITAGE IN SCIENTIFIC RESEARCH: A BIBLIOMETRIC ANALYSIS

The objective of this study is to examine the evolution of Ecotourism on Geoheritage through bibliometric analysis. Total of 53 papers pertaining exclusively to the domain of Ecotourism on Geoheritage, spanning from 2008 to 2022, were gathered from the Scopus database. This study examines the core aspects and features of Ecotourism on Geoheritage in academic research. It employs topic analysis, concurrence analysis, and timeline analysis of author keywords to investigate factors such as annual publication contribution, popularity, and focus. Furthermore, the analysis incorporates many productive entities, including journals, authors, institutions, countries, regions, and the mapping of significant collaboration links. These elements are utilized to determine the level of attention received by different entities in the field of Ecotourism on Geoheritage research. Furthermore, this study examines the citation structure of authors and journals and provides a detailed analysis of burst detection in cited authors, journals, and references. Ultimately, the study findings will be integrated with the present financial circumstances to delve further into future development obstacles and prospects. This bibliometric analysis reveals a consistent rise in yearly publications, a notable shift in emphasis towards financial inclusion, a prevailing presence of authors from Australia, and a growing number of international collaborations and publications from diverse sources. These findings indicate that the field of Ecotourism on Geoheritage is dynamic and holds promise for future scientific advancement. Hence, this exhaustive analysis of the Ecotourism on Geoheritage document not only examines the features and course of existing research but also assists researchers in identifying the appropriate research starting point and conducting thorough investigations.

Qualitative and Quantitative Study on Students’ Critical Thinking of Conceptual E-Scaffolding in Blended Learning Context

Critical thinking ability is one of the rational thinking capabilities that is highly required by students in solving problems or delivering an idea. In fact, however, students’ critical thinking skill is still categorized as low according to the previous studies. One feasible alternative to be done in developing students’ critical thinking ability is implementing electronic-based scaffolding. For that reason, there should be research to examine conceptual e-scaffolding in blended learning on Problem-Based Learning (PBL) model to observe the first semester students’ critical thinking ability at IKIP Budi Utomo Malang. This study employed mixed-methods with explanatory research design. The experimental group was given conceptual e-scaffolding treatment in blended learning on Problem-Based Learning model and the control group did not receive any additional treatment. For both experimental and control groups, the research subject was the students in the first semester at IKIP Budi Utomo Malang. As there were two research groups, 46 students belonged to the control group. The instruments utilized in this research were pre-test and post-test. The pre-test consisted of 18 multiple choice items with reliability coefficient of 0.735 and critical thinking post-test consisted of 14 essay question items with reliability coefficient of 0.762. In addition, semi-structured interview and think aloud guidelines were used. Quantitative data were obtained from the distribution of pre-test and post-test items. Meanwhile, qualitative data were retrieved from semi-structured interview and think aloud. The quantitative data were then analyzed using ANACOVA parametric analysis while the qualitative data were analyzed using path analysis model. The research findings indicated that: 1) the critical thinking ability of students who studied with conceptual e-scaffolding in blended learning on Problem-Based Learning was higher than those who studied with conventional method, 2) the implementation of conceptual e-scaffolding in the form of link map in Problem-Based Learning model influenced positively towards students’ critical thinking ability. This was reflected from the behavior and the way of thinking of students belonged to the experimental group indicating critical thinking ability development during the learning process. Furthermore, conceptual e-scaffolding in the form of link map helped the students to obtain information easily and utilized it in understanding concepts in geometrical optic material.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Qualitative and Quantitative Study on Students’ Critical Thinking of Conceptual E-Scaffolding in Blended Learning Context

Critical thinking skills are one of the rational thinking skills that students really need in solving problems or conveying ideas. In fact, students' critical thinking abilities are still relatively low. A feasible alternative for developing students' critical thinking skills is by implementing electronic-based scaffolding. This research uses mixed methods with an explanatory research design. The experimental group received conceptual treatment of e-scaffolding blended learning with the Problem-Based Learning model and the control group received no additional treatment. experimental group and control group, the research subjects were first semester students of IKIP Budi Utomo Malang. Because there were two research groups, 46 students were included in the control group. The instruments used in this research were pre-test and post-test. The pre-test consists of 18 multiple choice items with a reliability coefficient of 0.735 and the critical thinking post-test consists of 14 essay questions with a reliability coefficient of 0.762. In addition, semi-structured and think aloud interview guidelines were also used. Quantitative data was obtained from the distribution of pre-test and post-test questions. Meanwhile, qualitative data was obtained from semi-structured interviews and think aloud. Quantitative data was then analyzed using parametric ANACOVA analysis while qualitative data was analyzed using a path analysis model. The results of the research show 1) the critical thinking abilities of students who learn with conceptual e-scaffolding in blended learning in Problem-Based Learning are higher than students who learn with conventional methods, 2) the application of conceptual e-scaffolding in Problem-Based Learning. The link map form in the Problem Based Learning model has a positive effect on students' critical thinking abilities. This is reflected in the behavior and way of thinking of experimental group students which shows the development of critical thinking skills during the learning process. Apart from that, conceptual e-scaffolding in the form of a link map helps students obtain information easily and use it in understanding concepts in geometric optics material

Sparse CNN and Deep Reinforcement Learning-Based D2D Scheduling in UAV-Assisted Industrial IoT Networks

Unmanned aerial vehicles (UAVs) have been widely applied in wireless communications because of its high flexibility and line-of-sight transmission. In this study, we develop low-complexity and robust device-to-device (D2D) link scheduling in UAV-assisted industrial Internet of Things (IIoT) networks. First, we propose a sparse convolutional neural network (SCNN) model that uses the geographical map of transmission links as input. The model consists of three main blocks: generic feature filtering, speed–accuracy balancing, and deep feature processing. Unlike other state-of-the-art methods, the proposed SCNN directly processes the geographical map collected using a connected UAV. Second, we propose a deep deterministic policy gradient-based reinforcement learning model that processes the output feature map from the SCNN to optimize the D2D scheduling decision and maximize the achievable system rate in the long run. Extensive simulations revealed that the proposed scheme significantly improved the achievable rate over other benchmark comparison schemes, such as transmitters and receivers (T&R) density-based deep learning (DL), ResNet-based DL, VGGNet-based DL, random scheduling, and all-active schemes, respectively. The simulations also demonstrated that the proposed scheme reduces computational complexity. With reduced complexity and nearly optimal performance, the proposed solution can be more efficiently applied to large-scale and dense IIoT networks.

Towards learning-based energy-efficient online coordinated virtual network embedding framework

Network virtualization is a highly effective technology for resource sharing within data centers, enabling the coexistence of multiple heterogeneous virtual networks in a shared substrate network, thus achieving resource multiplexing. The efficient embedding of a virtual network onto a substrate network, known as the virtual network embedding (VNE) problem, has been proven to be NP-hard. In response to this challenge, this paper introduces a novel method, named PPO-VNE, which leverages deep reinforcement learning for virtual network embedding. PPO-VNE employs the Proximal Policy Optimization (PPO) algorithm to generate policies and efficiently coordinate node and link mapping. Furthermore, it adopts a hybrid feature extraction approach that combines handcrafted features with features extracted using graph convolutional networks. The proposed reward function takes multiple objectives into account, guiding the learning process. We implemented a prototype of PPO-VNE and conducted experiments based on the simulation environment, in which the substrate network has 100 nodes, with a probability of 0.1 generating edges between any two node, and eventually there will be about 500 physical links. We evaluate the performance of our PPO-VNE approach from the perspective of overall acceptance rate, overall revenue, revenue-to-cost ratio, maximum energy consumption per unit time and revenue-energy consumption coefficient. Comprehensive simulation results in different scenarios show that our PPO-VNE approach achieves the superior performance on most metrics compared with the existing state-of-the-art approaches, where the overall acceptance rate, overall revenue, revenue-energy consumption coefficient are increased by up to 6.4%, 21.3% and 41.4%, respectively, and the maximum energy consumption per unit time are reduced by up to 22.1%.

Virtual Network Embedding Over Multi-Band Elastic Optical Network Based on Cross-Matching Mechanism and Hypergraph Theory

The commercialization of 5G and the explosive emergence of new applications stimulate the exponential growth of network traffic and diversification of services. It is promising to integrate multi-band elastic optical network (MBEON) and network virtualization for large volume traffic transmission and highly diverse services. However, performing virtual network embedding (VNE) for network virtualization over MBEON faces the challenge of severe inter-channel stimulated Raman scattering (ISRS) effect, which complicates the underlying physical layer effect in the substrate network. In this paper, we investigate the ISRS-aware VNE over MBEON, where a cross-matching mechanism is proposed for virtual node mapping (VNM) and a hypergraph is introduced for parallel virtual link mapping (VLM). A lightpath-level integer linear programming model is first formulated. To integrate the cost and availability of VLM, which significantly affect the performance of the VNE under the ISRS effect, into the VNM process, the “virtual node-substrate node” mapping pairs are specifically evaluated through the cross-matching mechanism. Moreover, to tackle the couplings among multiple lightpaths induced by the wide spectrum ISRS effect, hypergraphs are used to model the ISRS effect-aware quality of transmission (QoT) constraints among multiple lightpaths. A hypergraph maximal weight independent set heuristic is presented for lightpath selection, which guarantees the obedience of basic constraints and generates near-optimal solutions. Experimental results show that the proposed methods decrease blocking ratio by more than 30% compared with the benchmarks with similar computational complexity.

An Optimal Satellite Selection Schema in Feeder Link Mapping for High-Capacity Scenario

An Optimal Satellite Selection Schema in Feeder Link Mapping for High-Capacity Scenario

Load-Balanced Virtual Network Embedding Based on Deep Reinforcement Learning for 6G Regional Satellite Networks

Regional satellite networks are capable of supporting denser coverage and more reliable communications in the target area and hence have been viewed as an essential part of the sixth generation (6G) communication system. Since satellite networks are time-varying and have limited resources, efficient resource management schemes are needed to accommodate massive and ubiquitous service requests. As a remedy, virtual network embedding (VNE) can enable diverse virtual network requests (VNRs) to share the same substrate network resources to improve resource utilization. However, existing works are few and mainly rely on heuristic methods, whose static embedding strategies cannot be optimized according to the resource state. In this paper, we propose a deep reinforcement learning (DRL) aided load-balanced VNE algorithm (DRL-LBVNE) for the regional satellite networks, where we first build a low-cost regional satellite network scenario and derive its multi-fold coverage constraints. Besides, we design a novel preprocessing scheme to reduce mapping failure, where the satellite network is divided into multiple mapping regions, and VNRs are only deployed in the mapping region with the lowest load. In the node mapping stage, the DRL agent can calculate the embedding probabilities of each physical node based on the environment state. Moreover, a comprehensive metric for path selection is presented in the link mapping stage. Simulation results show that the DRL-LBVNE algorithm outperforms the other five state-of-art algorithms in acceptance rate, resource utilization, and average delay, reflecting better adaptability to dynamic satellite networks.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Load Time and Link Mapping: Enhancing SEO experience for Private University Websites in Maharashtra

Load Time and Link Mapping: Enhancing SEO experience for Private University Websites in Maharashtra

Approximately Lossless Model Compression-Based Multilayer Virtual Network Embedding for Edge–Cloud Collaborative Services

Edge-cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge-cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this paper, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge-cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on Virtual Link Mapping Cost Estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

Minimum-cost embedding of virtual networks: An iterative decomposition approach

The ossification of the current Internet can be mitigated by network virtualization. The key idea of network virtualization is to abstract a logical service as a virtual network, which is mapped to the physical infrastructure as networked virtual machines. This process, known as virtual network embedding (VNE), entails two components: Node Assignment (NA), where virtual nodes are mapped to physical nodes as virtual machines (i.e., via node virtualization); and Link Mapping (LM), where physical paths are allocated to connect the embedded virtual nodes (i.e., via link virtualization). The existing VNE solutions based on Integer Linear Programming models and heuristic algorithms, unfortunately, fall in short in addressing either one of the two following perspectives: ILP-based models address NA and LM jointly, leading to optimal results but they suffer from extensive computation time; heuristic algorithms simplify the problem computationally but it lacks guaranteed solution quality. A large body of VNE solutions, particularly, adopt strategies like divide-and-conquer: NA and LM are resolved as two independent sub-problems in sequence. This paper is motivated by an important mathematical quest to better leverage this decomposition strategy: how can one identify the inherent link between the NA and LM sub-problems to obtain a decomposition approach without trading off solution quality? The major contributions of this work lie on three facets that fulfill this quest. First, based on a path-based VNE model, our primal–dual analysis reveals the process towards formulations of the NA, and LM sub-problems that preserve the link (as in ILP solutions). Second, we develop an iterative decomposition approach that employs merits from both worlds: the complexity of VNE (structurally and computationally) is simplified by decomposition while optimality or guaranteed closeness to the optimality is attained. Third, our evaluations confirm that the proposed schemes can achieve trade-offs in time and optimality, and outperform benchmark schemes by a large margin.

5G in healthcare: Matching game-empowered intelligent medical network slicing

Improving the healthcare system is imperative for increasing efficiency and reducing costs. The rapid adoption of Internet of Medical Things (IoMT) has facilitated a broad range of healthcare applications and services, from real-time and critical care monitoring to telemedicine. These services typically have highly distinct quality of service (QoS) requirement for communication networks and thus cannot be served with a uniform network. In such a context, network slicing technologies in 5G can be employed to create virtually independent and customized communication networks for these use cases to meet their QoS requirement. In this wok, we model network slicing for healthcare services as a virtual network embedding (VNE) problem and propose a two-sided matching theory-based virtual network embedding (MT-VNE) solution. In MT-VNE, four novel preference indexes are devised to construct differential preference lists. The deferred acceptance and modified shortest path-based algorithms are utilized to perform the virtual node and link mapping, respectively. Extensive simulations demonstrate that MT-VNE outperforms other baselines in terms of accepting more healthcare services and effectively utilizing physical network resources. Moreover, MT-VNE also significantly reduces service embedding time.

To improve the performance on disk load balancing in a cloud environment using improved Lion optimization with min-max algorithm

A key challenge was optimizing the speed of the network while maintaining impartiality. Power monitoring solutions have been developed to reduce these load imbalances. These techniques frequently need unique hardware or software on the participants' end. To accomplish the cloud computing system of load balancing, this study created an intelligent virtual machine programming scheme using a machine learning technique. The researchers introduced two methods that identify optimal probabilistic solutions to the issue after conducting an in-depth investigation. A min-max load balancing solution is generated by the second method, while the first approach reduces the load on the network's crowded Access Points (APs). Let's focus on the mapping of links in particular because the mapping of nodes was established a priori. This study provides an innovative and effective Improved Lion Optimization (ILO) with Min-Max Algorithm for enabling VNE in real systems consisting, breaking new ground in the research to employ Genetic Operators for parallelization. Load balancing & power conservation were two crucial goals that must be taken into account, and also the findings reduce the cost of processing time, the proposed system outperforms the sequentially one in terms of both goals. In addition, the adaptive capacity of the proposed algorithm was assessed across various substrate structural configurations. The load balancing goal was achieved; the typical data center usage was higher than that of other methods, reaching nearly 80%; the largest amount of virtual machine migration was reduced by 94.5%; the data center's maximum energy consumption was reduced by 49.13%.

The Influence of The 5E Learning Cycle Strategy with A Link Map on Students' Mastery of Physics Concepts and Science Process Skills

The Influence of The 5E Learning Cycle Strategy with A Link Map on Students' Mastery of Physics Concepts and Science Process Skills

The efficient employment of the space division multiplexing technique with OADMs for more flexibility and scalability in optical systems

Abstract The optical add drop multiplexers (OADMs) to be modeled and analyzed depends up on the simulation variables are demonstrated. Transmitted power per channel variations are studied against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. Received power from each channel variations are demonstrated against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The bit error rate variations are clarified against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The optical link map through fiber cable core is clarified. Total pulse broadening variations are illustrated against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The lighted signal/noise ratio variations are demonstrated against both receiver bandwidth and number of transmitted channels at transmission distance of 200 km and temperature of 25 °C. Channel capacity variations are also demonstrated against both receiver bandwidth and number of transmitted channels at transmission distance of 200 km and temperature of 25 °C.

Optimal resource allocation in practical quantum key distribution optical networks

In the application research of large-scale quantum communication network, one generally realizes resource allocation by constructing virtual service network and mapping it to actual physical space. In this mapping process, some assumptions are often made to simplify the model. For example, the key resource in the physical topology is assumed to be a fixed value, that is, the actual physical conditions and the performance differences of key supply caused by different protocols are ignored. This assumption may lead the network to fail to run appropriately in practical applications. In order to solve the above problems, from the perspective of link mapping, this paper proposes an improved virtual service mapping model and virtual service mapping algorithm with the quantum key distribution optical network as the underlying network, which makes it closer to the actual application scenario. On the one hand, by increasing the constraints of geographical location, the range from virtual nodes to the mappable physical nodes is reasonably restricted. On the other hand, from the perspective of hardware cost and actual key generation rate, the cost performance evaluation index is proposed to allocate and manage resources. In addition, by combining three mainstream quantum key distribution protocols (BB84, measurement-device-independent, and twin-field), we construct a universal virtual service mapping model in the quantum key distribution optical network, and realize the recommendation of the optimal protocol and the optimal allocation and management of resources.