Network Management Platform Research Articles

A Modular, End-to-End Next-Generation Network Testbed: Toward a Fully Automated Network Management Platform

A Modular, End-to-End Next-Generation Network Testbed: Toward a Fully Automated Network Management Platform

Research on Urban Monitoring Network Management Platform based on AI Data Stream Processing

In recent years, with the upsurge of the continuous development of high-tech information industry, the security industry has also made a new leap in technology, products, industry and so on. The traditional single small-scale monitoring and management software obviously cannot meet the actual needs of users in terms of system scale and functional requirements. This paper puts forward a network management platform for urban monitoring based on AI data stream processing. With the help of comprehensive processing and analysis of urban complex information, through network remote monitoring means, information supervision and public opinion trends are used to control social environmental security, timely warning and response to unexpected accidents, so as to ensure that governance departments can fully control and prevent possible problems such as road traffic, population distribution and disaster emergency.

Blockchain for Network Service Orchestration: Trust and Adoption in Multi-Domain Environments

In the coming years, blockchain technologies will be used in a variety of industries, including telecommunications. In this article, due to strict governance of telecommunication infrastructure, we propose a blockchain supported architecture, based on a permissioned distributed ledger (PDL) scheme, for a network management and orchestration platform. The main goal is to create a trusted environment for multiple-stakeholders, such as Cloud Service Providers (CSPs), a Mobile Network Operator (MNO), Vertical Service Providers (SPs), Legal and Regulation Authorities, and Responsible Ministry so that the life cycle of automated vertical network services (e.g., instantiation, scaling, termination, and migration/reallocation) can be managed securely and transparently in a multi-cloud and multi-domain environment. The proposed approach is also validated with an experimental Industry 4.0 scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, and time to write) of various service orchestrator (SO)-related instantiation metrics. At the end of the article, we present the main discussions on the evaluation results and existing standardization efforts for the convergence of BCN, Management and Orchestration (MANO), and network services for a given telecommunication infrastructure.

FLIP: A New Approach for Easing the Use of Federated Learning

Over the last few years, there have been several attempts to provide software tools for the development of federated learning (FL) models. However, both the complexity of the concept itself and the high entry barrier of these tools have meant that their adoption has been limited. Considering the related benefits, especially in terms of preserving data privacy, and the need for this type of solution in specific areas where data sharing is impossible, not only from a practical point of view but also from a legal and even ethical perspective, it is necessary to advance in solutions that allow its use to be democratised and its deployment to be extended. With this objective in mind, FLIP (Federated Learning Interactive Platform) has been developed as a comprehensive, easy-to-use fully functional web-based FL network management platform that eases and accelerates the usage of federated datasets by researchers in real scenarios. In this sense, FLIP has achieved a SUS score of 84.64, confirming a high level of perceived usability as expected. Taking this into account, FLIP can help increase the productivity and adoption of FL by a wider audience.

Construction of medical equipment maintenance network management platform based on big data

Introduction: Medical equipment is an indispensable part of hospitals. It is the basic condition and guarantee for the hospital to carry out medical services, scientific research, teaching, and other activities, and it plays an irreplaceable role in the entire medical process of the hospital. Therefore, the maintenance management of equipment is also the focus of our attention. In the past, traditional management methods such as paper were mainly used for equipment maintenance management, and it was difficult to share data.Methods: In today’s era of rapid development of information technology, we will use information technology to maintain and manage medical equipment. Through big data analysis and other technologies, the drawbacks of the existing traditional management methods are improved, so that medical equipment can be managed scientifically. By maximizing its functions, it can ensure the normal operation of medical facilities, improve the utilization rate and integrity rate of equipment, and reduce maintenance costs and unnecessary losses.Results: According to the research findings, based on the background of big data, a de-Bayesian network is used for data mining to build a medical equipment maintenance platform. Through the data in the platform, we can better discover the distribution and reasons of equipment maintenance, and at the same time conduct an analysis to provide reference for the formulation of preventive maintenance plans, reduce equipment failure rate and maintenance costs and improve equipment utilization. Through the survey of medical staff, we can also find that at least 40% of the people feel that the work distribution is more reasonable, and 45% of the people feel that the equipment failure rate and the time required for maintenance have been greatly reduced.Discussion: We can see that the network platform for medical equipment maintenance management built through big data is very feasible, which can help us work more effectively and improve work efficiency.

Community management tools on social networks in the context of the groundswell

This academic paper examines the role of social network management tools in the context of the bottom-up effect and analyses their capabilities. The tools facilitate efficient collection, analysis and visualization of community communication data and are critical for social network crisis situations. In conjunction with technological advances, artificial intelligence has also been integrated into the above platforms. The paper equally identifies specific social media management platforms that help to increase brand awareness, enhance purchase intent and are essential for effective user communication management. The research methodology combines qualitative and quantitative approaches, through the analysis of data sources from scholarly databases. The findings substantiate the importance of social media management tools for organizations and their necessity in the research field. The discussion explores the broader impact of social networks on community management and highlights the role of social network management platforms. The conclusion confirms that social network management tools are key to community management and present opportunities in a variety of areas.

Blockchain-Based Service Orchestration for 5G Vertical Industries in Multicloud Environment

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.

Research on saliency detection method based on depth and width neural network

Image saliency detection is to segment the most important areas in the image. Solving the problem of image saliency detection usually involves knowledge in computer vision, neuroscience, cognitive psychology and other fields. In recent years, as deep learning has made great achievements in the field of computer vision, the application of deep learning has also played a good role in image saliency detection. Therefore, algorithms based on deep convolutional neural networks have become solutions to image saliency The most effective method of detection. For researchers, improving the computational efficiency of neural network-based saliency detection algorithms generally starts from two perspectives. One is to tailor the network structure and combine traditional feature extraction methods for processing. The other is to use a lighter network to solve the saliency detection problem. Based on these two points of thinking, this paper proposes two efficient and accurate neural network-based saliency detection algorithms. In recent years, with the rapid development of multimedia and Internet technologies, a huge amount of picture information is generated on blogs, social networking or shopping platforms every day. Such a lot of information not only enriches people's lives, but also provides efficient and accurate network management platforms. The management of these image information brings difficulties. Therefore, how to understand and process these image information more intelligently and efficiently has become a hot topic for many image processing and computer vision researchers. Among them, the saliency detection technology plays a key role in solving the problem of intelligent understanding and processing of images. To put it simply, saliency detection is a technology to automatically calculate or detect the most important areas in an image, and its processing results provide a basis for understanding and processing the image content. Saliency detection is a basic problem in computer vision, neuroscience and visual perception. The algorithm detects and extracts the most interesting or significant areas in the image.

Design of English Teaching Sharing System Combining Internet of Things and Memory Mechanism

In the era of Internet of Everything, English teaching has evolved continuously undergoing radical changes. Informatization and modern technologies are continuously promoting the reform of education, and the application of these technological tools makes information sharing a reality. Timely information sharing contributes to the development of English teaching as a part of curriculum requirements. As a consequence of this rapid progress, the theoretical and practical research on IoT-based English teaching sharing systems is also increasing. In this paper, we initiate an exhaustive research of the popular methodologies and then adopt LSTM network management platform data to address the shortcomings of the current teaching system has memory mechanism issues. At the outset, the standard English curriculum is discussed to establish the background, and then, data fusion among various types of information is considered. This is done by completely utilizing independent and scattered heterogeneous data in the information platform without changing or minimizing the information available in the shared system. Then, the information fusion is thoroughly analyzed resulting in the proposal of a multisource and multigranularity information fusion method based on a neural network. The method implements a study considering multisource heterogeneous data, which is followed by carrying out of functional design and process design. The paper also conducts a study on CNN-based multisource information association and designs information association algorithms to customize the dataset. Finally, the study of multigranularity feature fusion based on CNN and LSTM is implemented. The design relevant to granularity calculation algorithm helps in the customization of the dataset. The multigranularity feature fusion is conducted integrating CNN and LSTM. The experimental results show that the proposed method effectively integrates the scattered teaching information in the system, which is conducive to improving the utilization rate of teaching resources and simultaneously promoting the development of teaching information.

Recent Advances in Data Engineering for Networking

This tutorial paper examines recent advances in data engineering, focusing on aspects of network management and orchestration. We provide a comprehensive analysis of standardization efforts as well as platform development activities related to data engineering driven network design. We then focus on the integration aspects of the data engineering ecosystem and telecommunication networks. The results of our tutorial investigation show that despite various efforts towards standardization and network management and orchestration platforms, there is still a significant gap in applying recent developments in the evolving data engineering world to the telecommunication domain. New advanced functionalities in data engineering as well as clear separations between the building blocks of data engineering pipelines within the proposed standardized architectures have been overlooked or not explored in detail by the standardization or platform development bodies in the telecommunication domain. Therefore, at the end of the paper, we discuss these gaps and research challenges in the context of future development processes for data engineering-driven network design and applications of data engineering concepts in telecommunication networks. We also propose several recommendations for early adoption of these technologies and frameworks in telecommunication infrastructures and platforms.

Research on torsional vibration monitoring system of ship power shafting

In this paper, the real-time monitoring technology of ship power system torsional vibration is studied. The photoelectric non-contact measurement method is used to measure the torsional vibration intensity of shafting, and the analysis service network management platform is established to realize the functions of real-time monitoring of shafting torsional vibration and upload and collect alarm data, through the front-end system, the torsional vibration strength and other parameters of the shafting are collected and transmitted to the background. The data of the background system is used to calculate and analyze, and the status of the shafting is alarmed. The system realizes continuous monitoring and data recording of torsional vibration index of ship power system, and ensures the operation performance and safe operation of ship power system. It provides theoretical and technical support for the future development of new technologies and related research, such as durability simulation and durability virtual test of ship power critical parts.

Research on saliency detection method based on depth and width neural network

Image saliency detection is to segment the most important areas in the image. Solving the problem of image saliency detection usually involves knowledge in computer vision, neuroscience, cognitive psychology and other fields. In recent years, as deep learning has made great achievements in the field of computer vision, the application of deep learning has also played a good role in image saliency detection. Therefore, algorithms based on deep convolutional neural networks have become solutions to image saliency The most effective method of detection. For researchers, improving the computational efficiency of neural network-based saliency detection algorithms generally starts from two perspectives. One is to tailor the network structure and combine traditional feature extraction methods for processing. The other is to use a lighter network to solve the saliency detection problem. Based on these two points of thinking, this paper proposes two efficient and accurate neural network-based saliency detection algorithms. In recent years, with the rapid development of multimedia and Internet technologies, a huge amount of picture information is generated on blogs, social networking or shopping platforms every day. Such a lot of information not only enriches people’s lives, but also provides efficient and accurate network management platforms. The management of these image information brings difficulties. Therefore, how to understand and process these image information more intelligently and efficiently has become a hot topic for many image processing and computer vision researchers. Among them, the saliency detection technology plays a key role in solving the problem of intelligent understanding and processing of images. To put it simply, saliency detection is a technology to automatically calculate or detect the most important areas in an image, and its processing results provide a basis for understanding and processing the image content. Saliency detection is a basic problem in computer vision, neuroscience and visual perception. The algorithm detects and extracts the most interesting or significant areas in the image.

A Knowledge Transfer Perspective on Front/Back-Office Structure and New Service Development Performance: An Empirical Study of Retail Banking in China

Aim/Purpose: The purpose of this study is to investigate the mechanism of the front/back-office structure affecting new service development (NSD) performance and examine the role of knowledge transfer in the relationship between front/back-office structure and NSD. Background: The separation of front and back-office has become the prevailing trend of the organizational transformation of modern service enterprises in the digital era. Yet, the influence of front and back-office separation dealing with new service development has not been widely researched. Methodology: Building on the internal social capital perspective, a multivariate regression analysis was conducted to investigate the impact of front/back-office structure on the NSD performance through knowledge transfer as an intermediate variable. The data was collected through a survey questionnaire from 198 project-level officers in the commercial banking industry of China. Contribution: This study advances the understanding of front/back-office structure’s influence mechanism on new service development activity. It reveals that knowledge transfer plays a critical role in bridging the impact of front and back-office separation to NSD performance under the trend of digitalization of service organizations. Findings: This study verified the positive effects of front/back-office social capital on NSD performance. Moreover, knowledge transfer predicted the variation in NSD performance and fully mediated the effect of front/back-office social capital on NSD performance. Recommendations for Practitioners: Service organizations should optimize knowledge transfer by promoting the social capital between front and back-office to overcome the negative effect organizational separation brings to NSD. Service and other organizations could explore developing an internal social network management platform, by which the internal social network could be visualized and dynamically managed. Recommendation for Researchers: The introduction of information and communications technology not only divides the organization into front and back-office, but also reduces the face-to-face customer contact. The impacts of new forms of customer contact to new service development and knowledge transfer between customer and service organizations call for further research. Along with the digital servitization, some manufacturing organizations also separate front and back-offices. The current model can be applied and assessed further in manufacturing and other service sectors. Impact on Society: The conclusion of this study guides us to pay attention to the construction of social capital inside organizations with front/back-office structure and implicates introducing and developing sociotechnical theory in front/back-office issue undergoing technological revolution. Future Research: As this study is based on the retail banking industry, similar studies are called upon in other service sectors to identify differences and draw more general conclusions. In addition, as the front and back-offices are being replaced increasingly by information technology such as artificial intelligence (AI), it is necessary to advance the research on front/back-office research with a new theoretical perspective, such as sociotechnical theory.

Actual Output Power Levels of User Equipment in 5G Commercial Networks and Implications on Realistic RF EMF Exposure Assessment

Here, the results on actual output power levels of the fifth generation (5G) user equipment (UE) operating in commercial 5G networks are presented. Using a network management platform, output power data for several UE were collected over fifteen days. Out of about 545 million power samples obtained from UE operating in two 5G commercial networks, the time-averaged output power, of relevance for electromagnetic field (EMF) exposure assessment, was found to be always less than 43% of the maximum time-averaged UE output power considering time division duplexing. In addition, 95% of the time-averaged power samples were found to be less than 8% of the maximum while the mean value was less than 2%. Despite very conservative assumptions in the applied method, the time-averaged 5G UE output power levels were found to be well below the maximum. These results are in line with the output power levels observed for 3G and 4G UE in previous studies and is of importance to characterize the actual EMF exposure of 5G devices during operation.

Analysis of air quality characteristics in Dezhou

Based on the national air quality network monitoring and management platform, the data from July 1, 2018 to June 30, 2019 were obtained, and major pollutants were analyzed. The study found that air quality grade in spring and summer in Dezhou region was better than that in autumn and winter; The air quality in Decheng district was better, and Xiajin county was relatively poor in the five monitoring points. According to the results, the corresponding control measures are proposed in order to provide reference for the joint prevention and control of air pollution in Dezhou.

A User‐Centered Multi‐space Collaborative Visual Analysis for Cyber Security

Based on a university's practice in upgrading its network management platform, presents a visual analytic system which integrates network topological space, IP space and network geographical space into a collaborative solution to help network administrators address difficulties in locating end user and troubleshooting. Throughout the development cycle, we worked alongside with users to clarify their actual demands and habitual operations through scenario application, interviews and a variety of evaluation. This user-centered approach guides us step by step to apply cyber security visualization and visual analytic technology to actual use.

Implementing optimisation functionality on network management platforms for new DSO business models

This study address the emerging need for distribution system operators (DSOs) to optimise distributed energy resources hosted in their networks. This study presents the active network management technology platform elements developed and tested by the authors to host DSO optimisation functionality and the requirements this addresses including: control platform architecture and properties, level of autonomy, solution speed for different use cases, fail-to-safes, real-time override, operator configuration and interface, market and system operator integration. The study also presents results of end-to-end lab test of the optimisation platform and applications with an objective evaluation of the outcomes and outlook for further development towards implementation by DSOs. Conclusions are drawn and presented on the technical, commercial business model and regulatory implications of implementing optimisation functionality to deliver new DSO business models.

Energy consumption optimization management mechanism based on drug green crowd data in biological pharmaceutical cloud environment

For satisfying the network trend and intelligent demand of biopharmaceutical, we proposed the energy optimization consumption and management scheme of the drug green crowd data in the biological pharmaceutical cloud environment. First, the biopharmaceutical process are mapped to the cloud platform, which can not only adapt to the revolutionary changes in the way of biopharmaceutical research and but also build a network management platform for pharmaceutical research and development. Secondly, based on the green crowd, we reconstruct the organization structure of the cloud platform, production process, and value chain-driven portfolio, etc. Then, we divide the core of the cloud platform architecture into five substages. The green screening, reorganization, and crowd data processing will be completed by the cooperation of these stages. Finally, the drug green crowd architecture is embedded into the time domain conversion interface and the state transition interface. In addition, the state energy consumption model of the biological pharmaceutical cloud platform is constructed. The experimental results show that compared with the traditional task-driven energy consumption management mechanism, the proposed management mechanism can ensure higher throughput, higher effective flow rate, and higher effective energy consumption ratio.

Supporting Unified Distributed Management and Autonomic Decisions: Design, Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities ($$\approx$$ź250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.

The effect evaluation on health education of computerized various forms of follow-up program for discharged patients with type 2 diabetes

Objective In the computer-oriented network management platform, discharged patients with type 2 diabetes was intervened with multi-form follow-up forms,and analyze the different effects at different stages of health education interventions. Methods Based on diabetes management center database follow-up system , various forms of follow-up program was used for 125 patients with type 2 diabetes who discharged from June 2014 to January 2015,they were followed for a period of six months, the self-management behaviors and blood glucose of patients at different stages,15 day, 1 month, 3 months , 6 months were analyzed and compared. Results After discharged 15 days ,1 month, 3 months ,6 months,the compliance to diet, exercise, blood glucose monitoring and medication of type 2 diabetes was statistically significant (χ2=8.274-28.634,P 0.05). Conclusions The computerized various forms of follow-up programs can improve the self-management behaviors and maintain a stable glycemic control of discharged patients with type 2 diabetes . Key words: Diabetes mellitus, type 2; Health education; Computerization; Self-management; Glycemic control