Research on Cloud Computing

Cloud computing and big data: Technologies and applications

ERP management information systems are widely used in the process of enterprise operation and development. By optimizing the allocation of management resources such as enterprise customers, project operations, material procurement, supply, and production, the utility of various resources in the enterprise is maximized, with integrated, systematic, and organizational management characteristics. Based on this, this article discusses cloud computing and ERP systems, analyzes the positive effects of cloud computing ERP systems on enterprises, and studies the specific applications of ERP systems in enterprises under cloud computing mode. At present, many large and medium-sized enterprises in China have established a complete ERP enterprise management information system. Considering the expansion of enterprise scale and the multiplication of enterprise management data, the existing ERP management information system has shown some limitations. With the rapid development of computer technology, the concept of cloud computing has been gradually promoted and applied. Based on the characteristics of cloud computing, ERP enterprise management information system began to consciously combine with the concept of cloud computing. This paper mainly introduces the characteristics and advantages of ERP enterprise management information system based on cloud computing mode, and analyzes its application effect in depth.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloud-based options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

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

In order to improve the ability of data retrieval in cloud computing environment, a reduced dimension classification algorithm of complex attribute big data in cloud computing based on deep neural network learning is proposed. The complex attribute big data under cloud computing is constructed by low dimensional feature set, and the complex attribute big data under cloud computing is analyzed by linear programming and fitting using grid clustering method. Big data samples of all complex attributes are sampled and trained to extract the associated features of big data, which is a complex attribute under cloud computing. The feature extraction results of complex attribute big data under cloud computing are inputted into the deep neural network learner for data classification, and the complex attribute big data dimensionality reduction classification under cloud computing is realized by combining big data fusion clustering method. The simulation results show that the accuracy of big data dimension reduction classification for complex attributes in cloud computing is high and the error rate is small.

For the increasingly prominent problems of wind turbine maintenance, using edge cloud collaboration technology to construct wind farm equipment operation and maintenance framework is proposed, digital twin is used for fault prediction and diagnosis. Framework consists of data source layer, edge computing node layer, public or private cloud. Data source layer solves acquisition and transmission of wind turbine operation and maintenance data, edge computing node layer is responsible for on-site data cloud computing, storage and data transmission to cloud computing layer, receiving cloud computing results, device driving and control. The cloud computing layer completes the big data calculation and storage from wind farm, except that, based on real-time data records, continuous simulation and optimization, correct failure prediction mode, expert database and its prediction software, and edge node interaction and shared intelligence. The research explains that wind turbine uses digital twin to do fault prediction and diagnosis model, condition assessment, feature analysis and diagnosis, life prediction, combining with the probabilistic digital twin model to make the maintenance plan and decision-making method.

Today, parents are working hard and looking after their kids at the same time. Due to the increasing security risks faced by children, both the parents need to monitor their child & #39; s activities. This paper proposes a system that uses an Android phone to monitor the child & #39; s activities. The system can also provide the parents with the necessary information about their child & #39; s safety. The paper also shows how this system can be used to create a safety zone around the child. The system can monitor the child & #39; s activities and create a safety zone around the child. It can also provide the parents with the necessary information about the child & #39; s location. The system is equipped with a video camera that can be operated using the instructions from the Android phone & #39; s software hand function. The video camera can also be used to capture the child & #39; s movements. This project & #39; s major goal is to create an IoTbased Child Monitoring System that will allow parents to watch and detect their children & #39; s activities even when they are away from home. It is an innovative, smart, and protected Child Monitoring System designed to efficiently nurture a newborn. This approach takes into account all of the minor elements necessary for the child & #39; s care and protection at the institution and elsewhere. The usage of technologies/methodologies such as the Internet of Things (IOT), Live Video Monitoring System, Cloud Computing (Data Storage), and User Friendly Web Application helps to build smartness and innovation (for User Controls). Different Sensors/Modules are fitted to the child in order to detect each and every activity. All data collected from sensors/modules will be kept in the cloud and examined on a regular basis.

The concept of cloud computing has recently changed how hardware, software, and information are handled. However, security challenges and credibility requirements have never changed and may have increased. Protecting cloud computing and providing security for its resources and users is one of the critical challenges. As a result, most users are afraid to use their resources, because many security problems must be met. For example, authentication and reliability are major security constraints and must be provided in a cloud computing environment. There is a wide range of authentication protocols in use, but the researcher has recommended the Kerberos protocol to represent and test it in a complex environment such as a mixed cloud environment. A model has been developed to implement Kerberos authentication in a hybrid cloud computing environment to securely access the cloud computing services provided. This model is represented using the OPNET Modeler 14.5 simulation system. The network efficiency was measured before and after the hacker. Findings presented in this research are supporting the ability of the Kerberos protocol to prevent illegal access to cloud computing services, whether from within the private cloud or the public cloud. While maintaining the efficient performance of the network.

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).

cloud computing is a new generation computational manner. In this technique the way of computing is transformed into distributed computing, therefore the concept of cloud is used where the efficient computational experience and scalable computing is required. Not only has the cloud provided the scalable computing it also provides the scalable storage units. Therefore a significant amount of data is arrived in these units to handle them, among various kinds of storage the unstructured data storage is also a part of entire cloud data storage i.e. social networking text data, images and other electronic form of documents. Thus in this presented work a survey is introduced for cloud data storage, and their cluster analysis for utilizing the data into various business intelligence applications. in addition of that a new model of cluster analysis of data is proposed which provides the clustering as service. Keywords— cloud computing, cluster analysis, data mining, social networking, text clustering.

Developments in service oriented architecture (SOA) have taken us near to the once fictional dream of forming and running an online business, such commercial activity in which most or all of its commercial roles are outsourced to online services. The novel concept of cloud computing gives a understanding of SOA in which Information Technology assets are provided as services that are extra flexible, inexpensive and striking to commercial activities. In this paper, we concisely study developments in concept of cloud computing, and debate the advantages of using cloud services for commercial activities and trade-offs that they have to consider. Further we presented a layered architecture for online business, and then we presented a conceptual architecture for complete online business working atmosphere. Moreover, we discuss the prospects and research experiments that are ahead of us in realizing the technical components of this conceptual architecture. We conclude by giving the outlook and impact of cloud services on both large and small businesses.