Design of Enterprise Management System Based on Edge Computing Architecture

Edge computing system is facing real challenges with the evolution of new information and communication technologies (ICT). In addition, with the advent of Internet of Things (IoT) and real-time services, current edge computing system is becoming an architecture that is inappropriate for some services that require ultra low-latency and very high throughput. In order to meet all the requirements of these services, a new edge computing architecture is required. For this purpose, Home Edge Computing (HEC) architecture has been proposed. However, edge computing system has encountered some issues such as resource limitation on home servers. Furthermore, most distributed systems (e.g. HEC) operate in online mode. In other words, once failures occur in the Internet (remote servers), applications/services will be not longer accessible. In this paper, we propose HEC-NerveNet architecture, a technique based on the HEC architecture and NerveNet technology (HEC-N), to allow users to have continuity of services (resilient), even if they do not access to the remote servers (cloud computing). As a reminder, NerveNet is a resilient distributed architecture that we proposed in 2011 after the natural disaster in Japan. It allows maintaining connectivity and services in case of network failure. In addition, NerveNet solution allows the automatic clustering, and fast recovery thanks to the mesh topology on NerveNet network. This can overcome the need to manually create clusters in the HEC architecture. In the simulation, we prove that our proposal (HEC- N) is very suitable for resilient architecture and the need for future generation networks (beyond 5G/6G networks) with improving the metrics such as ultra-low latency and very high throughput compared with the current HEC and NerveNet systems.

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

In the article on the basis of semantic analysis (the terms "costs", "management", "cost management", "enterprise", "cost management in the enterprise", "method") and determining the place of cost management methods in the cost management system at the enterprise such management methods. The chronology of development of methods of management of expenses at the enterprise and their characteristics is resulted. The classification of cost management methods of business entities is developed and proposed separately. The crisis of the national economy, which covered a period of twelve years, significantly changed the approaches of domestic entrepreneurs to management. Problems with sales, unprofitable activities required a focus on crisis management (crisis financial management), loss control, maintaining a stable level of profit, which was achieved primarily through cost management. The purpose of the article – based on a critical assessment of scientific developments in the field of cost management to determine the essence, characterize and develop a classification of cost management methods in the enterprise. The theoretical and methodological basis of the study were the works of domestic and foreign scientists, leading scientists and practitioners in the field of cost management in the enterprise. General theoretical methods were used in the research process. The system method was used in the study of theoretical aspects in the field of cost management. The method of analysis was used in the study of the most popular methods of cost management and their chronology of development. With the help of abstract-logical method – substantiated and presented conclusions and proposals for improving methods of cost management in Ukrainian enterprises. The article defines the essence of cost management methods based on semantic analysis of the term "cost management method" using a number of economic terms to form its structure, as well as determining the place of cost management methods in the cost management system in modern enterprise. A brief description of the most common methods of cost management in the financial and economic activities of economic entities, indicating their positive and negative sides. Based on the above characteristics, a classification of cost management methods was developed and proposed.

The rapid development and wide application of computer technology have promoted the development of all walks of life and played a positive role in promoting the construction of the government in China. As an important participant, manager and service provider of a country, the enterprise management system plays an important role in the process of informatization in our country. The automation and standardized management of enterprise management business, and give full play to the Internet and the existing information foundation, effectively carry out effective communication and cooperation to meet the needs of modern economic and government development. In accordance with the instructions of the national industry and commerce department, an online electronic information platform for enterprise operation and management shall be established. Enterprise management is a collection of enterprise name verification, private operation, domestic capital management, foreign capital management, trademark management, advertising supervision, case handling, enterprise early warning, credit file, case management, contract management, daily management, and fee management.On this basis, the specific design of several main business units, such as enterprise registration, daily management, and case management, is proposed.

Under the Intelligent Management Environment, in order to promote the enterprise management efficiency and release the enterprise management cost, it is necessary to construct the modern enterprise management system based on the intelligent technology, to realize the intelligent upgrade and all-round development of enterprise management. C/S structure is an open mode, which can improve the efficiency of enterprise management. By studying the present situation of enterprise management, deeply discussing and analyzing the enterprise management, this paper puts forward an enterprise management system with C/S structure, to realize the automatic management of enterprise management, such as data collection, information inquiry and data statistics process, and improve the efficiency of enterprise management in an all-round way.

With a rapid development in the field of internet of everything the number of devices using the internet has also increased this will affect the speed of internet. This will lead to the high bandwidth, slow response, poor security and poor privacy in the cloud computing models. Due to the increase in the internet of things there is a very limitation arising for the cloud computing. So there is a lead towards edge computing. It is a computer paradigm for performing calculations at the edge of the devices. It will be closer to the user and closer to the use of data there need only a small response time than the cloud. This article will go through an introduction of edge computing. The architecture of edge computing. The advantages of edge computing. The applications where it is used. A comparison between edge and cloud and finally towards the conclusion.

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

Introduction . The rational organization of the management system as a whole, and in the context of certain types of management activities, such as financial, strategic, innovation, personnel management, etc., now becomes not an additional business advantage, but an objective condition for the existence of an enterprise. But since the market economy, within which the modern production of business, financial, investment processes must be carried out by modern business entities, focused primarily on the needs of the final consumer and guided by the demand and supply trends, it is necessary for the management of domestic enterprises to distinguish market management as an applied bases for the functions of planning, organization, motivation, coordination and control over economic activities, taking into account market trends and customer-oriented needs of all categories of stakeholders . Purpose . The purpose of the article is to identify the features of market management in enterprise management systems in Ukraine. In order to realize the goal set within this study, it is necessary to fulfill a number of the following tasks: to offer an author’s definition of the concept of market management; to establish the list of goals and results of effective realization of market management processes for domestic enterprises in modern economic conditions; to identify obstacles of the integration of market management practices into enterprise management systems in Ukraine. Methods. Method of theoretical analysis, monographic method, method of generalization, abstract-logical method are used. Results. Under market management, it is proposed to understand a separate form of management activity that is dynamic and flexible, and which is dominated by the type of innovation management, able to respond in a timely manner to the market conditions changes, new conditions of competition and quickly transform the mechanisms of enterprise activity, taking into account the requirements of the environment and in line with the expectations of stakeholders. The list of goals and results of effective realization of market management processes for domestic enterprises in the modern economic conditions is established. The main ones are: orientation on the needs of customers and other categories of stakeholders, the formation of price and production policy, depending on the trends of supply and demand; cost optimization and transition to innovative production technologies; flexibility in making managerial decisions and information transparency. Originality. Author’s definition of the concept of market management is proposed. The list of goals and results of effective realization of market management processes for domestic enterprises in the modern economic conditions is established. The obstacles on the way to integration of market management practices into enterprise management systems in Ukraine are identified. Conclusion . The obstacles of the integration of market management practices into enterprise management systems in Ukraine are identified. Thus, obstacles to the active use of market management practices were: business entities resource constraints, the lack of skilled personnel, lack of information on market trends necessary for wise management decisions, traditions of doing business that are difficult to undergo transformation, etc.

<p>The vigorous development of Internet finance is very important for the sustainable development of China's economy. In recent years, Chinese government has vigorously supported the development of enterprises, and issued a series of policies related to finance, tax and financing guarantee support. For enterprises, this is not sufficient. In addition to the support of external policies, enterprises need more self-examination. The financial management system of many enterprises is not perfect and the quality of financial management is low, which will have a negative impact on the operation and management of enterprises and can not effectively guarantee the quality of financial information of enterprises.</p>

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

The convergence of edge computing and microservices architecture represents a transformative shift in how organizations process and manage distributed data systems. This article explores the limitations of traditional cloud-centric approaches and presents edge computing as a solution to address these challenges. Through a detailed analysis of real-world implementations across various sectors, including autonomous vehicles, smart cities, and industrial automation, the article examines how edge computing integrated with microservices architecture improves system performance, reduces latency, and optimizes resource utilization. The article investigates deployment challenges and security considerations and provides insights into effective monitoring strategies. Furthermore, the article explores emerging technologies and future research directions that will shape the evolution of edge computing and microservices integration.

At present, the constantly changing social environment creates opportunities for the development of enterprises, but also makes them face more risks. Corporate compliance management can help enterprises effectively avoid risks and maintain long-term and stable development. In order to improve the internal and external supervision efficiency of enterprises, this paper uses ADR mechanism to design the compliance management and supervision system of enterprises under the Internet environment. On the basis of collecting relevant data of enterprise compliance management by using information technology, the existing problems are discussed through big data analysis, and the online linkage management and supervision system of enterprise compliance is constructed. Then the ADR mechanism is used to analyze and deal with the conflicts and disputes existing in the enterprise operation, and the compliance management organization is constructed on the basis of the establishment of compliance culture, so as to form a benign supervision mechanism.

The large-scale deployment of photovoltaic installations at geographically remote sites underscores the need for effective and reliable monitoring systems. The monitoring tools should be capable of managing numerous devices that generate large amounts of data. One of the major challenges is to ensure interoperability with equipment and guarantee the reliability of photovoltaic system operations. The present work aims to propose monitoring architecture that can handle remote photovoltaic plants and integrate operation and maintenance strategies. The architecture embeds a distributed data flow, an anomaly detection pipeline in addition to performance indicator calculation in accordance with IEC 61724-1, and a real-time notification system. The architecture is based on a microservice architecture that integrates edge computing for local data acquisition and preprocessing. System validation was carried out to assess latency, data completeness, and data consistency. The results show stable latency with an average of 3.4 s with data completeness of 100% and 99.57% consistency. These findings demonstrate the ability of architecture to monitor remote plants, highlighting its suitability for large-scale deployment and the integration of advanced analysis. • A scalable edge computing and microservice architecture for PV monitoring. • Ensure multi-vendor interoperability via protocol adapters and data standardization. • Provides KPIs compliant with IEC 61724-1, anomaly detection pipeline and predictive maintenance insights. • Real-time data visualization for multi-site PV systems and notifications.

This paper proposes a novel edge-computing based structure to support learning-based decision-making in industry manufacturing field. This structure consists of four functional layers, respectively realizing model establishment, task allocation and task processing work. In order to take full advantage of the distributed computing resources at the edge, the manufacturing computing task can be further decomposed into several sub-tasks, separating the complex computing problem with large problem size into regional scheduling ones with much smaller problem size. All the sub-tasks are allocated to the edges, accomplished by the algorithm deployed on computing devices of region-related edge node, which contributes to faster data-processing and problem-solving speed. A simulation test has been performed in which a multi-AGV scheduling problem was solved according to a distributed reinforcement learning method configured in such edge computing architecture. The objective of each edge node is to acquire AGV schedule of related region that minimizes the makespan. Simulation results demonstrate that this distributed edge computing system can be enabled to learn satisfying solution and converge much faster when it is compared with conventional method applied in centralized architecture.

Edge computing is a technique of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data in the systems. And, cloud computing is a service that delivers on-demand self-service, broad network access, resource pooling, rapid elasticity or expansion, which is trending in today's technology-driven world. With the advantage of flexibility, storage, sharing, and easy accessibility, cloud is being used by major players in IT (information technology). This chapter highlights cloud/edge computing architecture and its current issues and challenges from technological and organizational aspects. A brief introduction of edge computing architecture with similar technologies along with its service models is discussed. A few counterexamples of cloud computing architecture are showed. Organizational aspects of cloud computing architecture, as well as IBM and Oracle reference cloud architecture, are briefly presented. Some emerging issues and challenges associated with cloud/edge computing on its utilization are also elaborated.