Design of Distributed Network Mass Data Processing System based on Cloud Computing Technology

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

Cloud computing has become a generic umbrella term for the flexible delivery of IT resources—such as storage, computing power, software development platforms, and applications—as services over the Internet. The foremost innovation is that the IT infrastructure no longer lies with the user, breaking up the previously monolithic ownership and administrative control of its assets. The combination of cloud computing and semantic technologies holds great potential. In this chapter, we analyze three ways in which cloud computing and semantic technologies can be combined: (1) building on cloud computing technologies, e.g. from the area of distributed computing, to realize better semantic applications and enable semantic technologies to scale to ever larger data sets, (2) delivering semantic technologies as services in the cloud, and (3) using semantic technologies to improve cloud computing, in particular to further improve automatic data-center management. For each of these dimensions we identify challenges and opportunities, provide a survey, and present a research roadmap.

The paper proposes the method of building the electric power marketing business application system based on the cloud computing and microservices architecture technologies to solve the issues of the current system with the concentrated infrastructure and the monolithic architecture which are difficult to support the business processing in very large scale and the evolution of the electric power marketing business. The paper first introduces the current electric power marketing business application system and highlights its main drawbacks. Then it proposes the method to build the innovative electric power marketing business application system with the main technical characteristics as follows: the IaaS part of the application system has the distributed infrastructures with the IT resource elastic management and powerful horizontal scalability brought by the cloud computing technology; its PaaS part is the service platform with the components of the data processing, information integration, application building, and cloud service center to provide the several cloud services, such as one-click deployment, flexible scaling, fault self-healing, gray distribution, full-link monitoring and other cloud service capabilities; and its SaaS part has the microservices architecture with the features of servitization, componentization, decentralization, independent deployment etc. Finally, the suggestions are given to design the microservices for the electric power marketing business application.

Cloud computing and big data: Technologies and applications

With the rapid development of virtualization, cloud computing and other technologies, and their gradual application in various industries, how to use the cloud computing infrastructure well and make the cloud computing play the maximum capacity is an important issue in cloud computing technology. In this context, the concept of "cloud-native" was born. With the development of cloud-native technology, its related research has gradually become a field of concern and research for scholars, but there is still a lack of research on the development trend of cloud-native technology. Therefore, in this paper, 904 high-quality papers were downloaded from web of science and correlation analysis was conducted by Citespace and VOSviewer with the help of bibliometric research methods, including literature quantity analysis, co-citation analysis, keyword co-occurrence analysis and research hotspot analysis.

With the constant development of socialist modernization construction in our country, our country's computer information technology has made effective progress, and makes the world into an information age. People's production and life style experience a series of change. However, with the emergence of information diversity and multi-user mode, traditional computer information technology already can't satisfy people's needs, and the development and application of cloud computing technology come to the stage. This article focuses on analyzing the big data user behavior engine design under cloud computing technology, covering big data flow management, multi-user system design, and other aspects to explore the effect of the system test. In recent years, China's mobile Internet technology has made full development, which makes the Internet operators in our country face a new development opportunity and began from traffic management to flow management. The law of user behavior is analyzed to explore the real demand of market and the users. In order to fully meet the changing needs of users, operators must constantly develop and launch new products and strengthen the function of computer technology. Cloud computing technology is such kind of engine system which can satisfy the analysis and processing of huge amounts of data. RESEARCH ON CLOUD COMPUTING SYSTEM OVERALL DESIGN The overall architecture of cloud computing system This study mainly uses cloud computing technology’s huge amounts of data computing to set up complete mobile Internet data mining analysis system. Realize the analysis for Internet user behavior engine, and according to the user's preferences online habits and behavior, provide users with targeted personalized service and form a unified organic whole of data collection, analysis and service type and marketing strategy to improve enterprise's marketing efficiency. In addition, cloud computing system mainly realizes data collection with the help of FTP server, and makes distributed computing and data batch processing at the system interface. Large data shall be deposited in Hbase database. The system can not only realize mass data storage, but those unstructured data storage. Then through Hive integration layer and summary EIL processing, use MapReduce data analysis model to transmit the processing result into the database. The system’s overall structure is shown in Figure 1: 3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) © 2016. The authors Published by Atlantis Press 1351 Figure 1 The overall architecture of cloud computing system System topology and function distribution System topology mainly refers that a server is taken as Hapdoop platform’s master node server, and the others are Hapdoop platform’s node servers. In general, node server can be dynamically extended according to the actual need, and the master node server not only assigns tasks and flow to node server, but also monitors the work execution of the node server. More than one node server participates to complete the task, and it can improve data processing efficiency. The master node server’s software running status is shown in Figure 2: Figure 2 Master node server function structure

Cloud computing can be regarded as the integration of grid computing and virtualization technology: namely the use of grid distributed computing processing power of IT resources and build into a resource pool, plus on a mature server virtualization, storage virtualization technology make users can real-time monitoring and deployment of resources. Cloud computing and advanced manufacturing technology is facing many challenges during the process of development in the future, including security and privacy will become a primary issue. This study focused on relations with the important areas of emerging industries, the fast scheduling network resources in the cloud computing and advanced manufacturing technology key issues, as well as cloud computing security management issues related to cloud computing and advanced manufacturing technology.

Building Smart Power Utilization Service System by Using Business Process and Cloud Computing Technologies

Cloud computing has transformed the IT field by offering adaptable and versatile resources to cater to the increasing demands of businesses and organizations. Virtualization technologies are instrumental in facilitating the efficient deployment and management of resources within cloud environments. However, there are notable concerns regarding the security implications of virtualization in the cloud. This research paper thoroughly examines the security aspects of virtualization technologies in cloud computing, primarily focusing on identifying potential weaknesses, risks, and strategies to mitigate security threats. Additionally, the study investigates the security features and mechanisms provided by leading virtualization platforms and management tools. It scrutinizes access controls, isolation methods, network security, data protection, and integrity mechanisms offered by virtualization technologies to safeguard the cloud infrastructure and customer data. Furthermore, the paper addresses emerging security concerns associated with containerization technologies, encompassing vulnerabilities related to container escape, risks stemming from shared kernels, and issues concerning image integrity. It explores the effectiveness of container security measures, such asisolation, sandboxing, and access controls, in reducing these risks. Lastly, the paper summarizes the main findings and provides recommendations to enhance the security of virtualization technologies in cloud computing. It emphasizes the importance of continuous monitoring, regular security updates, robust access controls, and threat intelligence integration to mitigate security risks and uphold a secure cloud infrastructure.

Traditional classification algorithms can be well applied to limited data sets, but the application effect of uncertain data sets was poor. Therefore, this paper proposed a cross-source education information classification model based on cloud computing technology, which aimed to provide support for education information services in the cloud computing environment. Firstly, based on the analysis of the structure and function of the cloud computing platform, this paper expounded the cloud computing service mode and its deployment mode and gave a multisource information processing method based on the cloud computing center combined with the characteristics of information distribution in the cloud computing environment. Secondly, by analyzing the types of educational information resources, this paper summarized the feature extraction of educational information using data mining technology and gave the classification method of educational information based on text features. Finally, a cross-source education information classification model in the cloud computing environment was designed. The experimental comparison showed that the method proposed in this paper can effectively classify the multisource education information under the cloud computing platform. Compared with other traditional classification models, this model not only had higher classification accuracy but also can achieve better classification efficiency. The classification model proposed in this paper can provide a reference for the classification of other information resources in the cloud computing environment.

With the rapid development of the Internet and cloud computing applications, the construction of a digital library in the cloud computing environment has been put on the agenda. Construction and management of digital libraries using cloud computing technology, can effectively utilize resources, reduce costs, achieve better information sharing, and improve the service function of the library. In this paper, studies digital library based on cloud computing. Firstly, introduces the characteristics and establish principles of digital library based on cloud computing; secondly, elaborated the concept and work principle of cloud computing; then, describes the key technology of cloud computing, and a detailed introduction of virtual storage technology in digital library; and finally introduced cloud computing digital library service model, including cloud software as a service layer (SaaS), cloud platform as a service layer (PaaS), cloud infrastructure as a service layer (IaaS).

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing.

Cloud computing is a new paradigm where data and services of Information Technology are provided via the Internet by using remote servers. It represents a new way of delivering computing resources allowing access to the network on demand. Cloud computing consists of several services, each of which can hold several tasks. As the problem of scheduling tasks is an NP-complete problem, the task management can be an important element in the technology of cloud computing. To optimize the performance of virtual machines hosted in cloud computing, several algorithms of scheduling tasks have been proposed. In this paper, we present an approach allowing to solve the problem optimally and to take into account the QoS constraints based on the different user requests. This technique, based on the Branch and Bound algorithm, allows to assign tasks to different virtual machines while ensuring load balance and a better distribution of resources. The experimental results show that our approach gives very promising results for an effective tasks planning.

According to current status and problems of electronic medical records management system, it combines with the current the latest information technology, cloud computing, and it explores the solution of electronic medical records management system and puts forward the new ideas and methods; Based on the literature and information on cloud computing technology, we summarize the cloud computing theory, architecture, application model, etc. and select the appropriate application platform for the electronic medical records ; Based on the Google cloud computing technology , it focus on Google cloud computing platform Google App Engine and its solution Plan, it makes the research of collaborative electronic medical record system based on Google App Engine. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4392

This paper begins with an analysis of the advantages of cloud computing technology in the teaching of experimental courses in colleges and universities. In order to make full use of the role of cloud computing technology in computer-assisted teaching, this paper combines the cloud computing technology with the teaching reform of computer basic courses in colleges and universities. The Design and Implementation of Computer Experiment Course Teaching in the Environment of Cloud Computing Technology. The program builds a computer experiment teaching environment on the basis of cloud computing technology and launches a new teaching reform model based on cloud computing technology. On the basis of cloud computing technology, this paper discusses the practical cases of collaborative learning model and shows that the teaching reform design of computer experiment courses based on cloud computing technology can achieve good experimental teaching effects, further improve the quality of computer experimental teaching, and make students’ innovation and practice. The continuous improvement of capabilities is of great significance to the construction of a resource-saving experimental environment, curriculum teaching reform, and innovative experimental teaching modes.