Cloud Computing Service Platform Research Articles

Governance in the Free Cross-border Flow of Data

Every part of life, work and friendship has been inseparable from the Internet. When we casually click on the links on the network, when we need to enter personal information on the network due to work and policy, when enterprises are used to storing most of the data in the emerging cloud computing service platform, when the official website of the core departments of the country was maliciously attacked. We are facing more frequent information leakage in the era of big data. Especially with the trend of multinational corporations, regional interoperability, and the integration of the world economy, data and information can flow across borders without obstacles. Undoubtedly, cross-border business trade, project co-operation, geo-location, and server tracking are necessary, but the large amount of data recording, flow, and tracking brings great risks to personal privacy, business secrets, and the maintenance and stability of national security. As a result of the growing awareness of the risks and drawbacks of cross-border data flows, jurisdictions and regions have developed legislation and rules to balance the free flow of data and cross-border security.

Construction of Network Multimedia Teaching Platform System of College Sports

With the development of computer technology and related disciplines, the application of multimedia technology to education is rapidly becoming the mainstream technology of educational technology. This article mainly researches the construction of college sports network multimedia teaching platform system. The physical education management module is the main module in this sports network teaching platform. Therefore, the physical education management module is tested in the testing process. Each module adopts a black box test method. In the system analysis, by dicing, slicing, rotating, and other operations on the data warehouse constructed with the student's performance data, the data are observed from different angles, which is convenient for the teacher to understand the teaching effect. This article adopts the method of grade evaluation to finally determine a number of indicators that are not contradictory and nonrepetitive. The end of a teaching unit or a series of teaching activities must be evaluated at different levels. The biggest difference between process evaluation and summative evaluation is that it not only attaches importance to objective measurement and quantitative analysis but also attaches importance to the subjective judgment and qualitative analysis of both teaching parties, which requires a combination of the two. The test environment of the system is to simulate the real operating environment of the system. A cloud computing service platform is built in the school's central computer room to provide services such as dynamic resource scheduling and dynamic load balancing. There are very few schools that use college sports networks to develop educational functions, only reaching 7.8% of the total number of campus networks. The results show that the multimedia teaching platform greatly promotes the teaching effect of college physical education.

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

If the product supply shortage occurs during the sales period, customers will turn to other companies, so the enterprise lose the sales opportunity. If the enterprise can predict product demands, and manages the product sales by using the supply chain management prediction system to improve the bottleneck of the inventory, the hot sales product can have more critical time utilization, and the inventory status can be reflected quickly by Internet of Things (IoT). To overcome the problem that the replenishment model cannot show the actual quantity of products on the store shelves, in the paper, we propose an intelligent agent-based prediction system, which serves as a framework to construct an integrated prediction system through the use of radio frequency identification (RFID) technology to design the intelligent product prediction shelf to extract product messages, and the service oriented architecture to develop prediction information to recommend products to the customer. The result of the paper proposes an agent-based cloud computing service platform in IoT and intelligent agents with SOA as backend cloud services. To build a prototype prediction system with performance analysis, it can be proved that the prediction system architecture for intelligent agent-based prediction system could improve operation performance and effectively enhance customer service quality for hot sale products.

Construction of performance monitoring model for cloud computing service platform based on label technology

To solve the problems of low service efficiency, large change in total system requests and high blocking probability of the current performance monitoring model for cloud computing service platform, a performance monitoring model for cloud computing service platform based on label technology is proposed. By using the model, the concept and characteristics of cloud computing are analysed, the purpose of performance management and performance monitoring structure of cloud computing service platform are summarised, the service efficiency, time characteristics, resource utilisation and efficiency compliance of cloud computing service platform are evaluated, the structure model of cloud computing service platform is established, and the label technology is introduced, to build the performance monitoring model for cloud computing service platform. Finally, the performance monitoring of cloud computing service platform is realised. The experimental results show that the proposed model has high service efficiency, stable change of the total number of system requests and low blocking probability, which verifies the validity and superiority of the proposed model, and provides a new idea for the development and progress of performance monitoring technology of cloud computing service platform.

Construction of performance monitoring model for cloud computing service platform based on label technology

To solve the problems of low service efficiency, large change in total system requests and high blocking probability of the current performance monitoring model for cloud computing service platform, a performance monitoring model for cloud computing service platform based on label technology is proposed. By using the model, the concept and characteristics of cloud computing are analysed, the purpose of performance management and performance monitoring structure of cloud computing service platform are summarised, the service efficiency, time characteristics, resource utilisation and efficiency compliance of cloud computing service platform are evaluated, the structure model of cloud computing service platform is established, and the label technology is introduced, to build the performance monitoring model for cloud computing service platform. Finally, the performance monitoring of cloud computing service platform is realised. The experimental results show that the proposed model has high service efficiency, stable change of the total number of system requests and low blocking probability, which verifies the validity and superiority of the proposed model, and provides a new idea for the development and progress of performance monitoring technology of cloud computing service platform.

Design of mine safety dynamic diagnosis system based on cloud computing and internet of things technology

A dynamic diagnosis system of mine safety based on multi-data fusion is designed by combining the advantages of cloud computing and the Internet of things. The system’s framework is built with a three-tier model, and the construction of the cloud computing service platform provides essential support for mass data storage, processing, and security diagnosis reasoning. Simultaneously, using the relational database SQL Server 2017 and the object-oriented language C# to complete the design of an expert knowledge base and reasoning mechanism, and establish the diagnostic scoring system in the way of weighted sum. Therefore, based on the logical matching and reasoning between the collected data and the safety rules, the dynamic diagnosis of mine safety is realized.

A Reinforcement Learning-Based Resource Allocation Scheme for Cloud Robotics

In recent years, robotic systems combined with cloud computing capability have become an emerging topic of discussion in academic fields. The concept of cloud robotics allows the system to offload computing-intensive tasks from the robots to the cloud. An appropriate resource allocation scheme is necessary for the cloud computing service platform to efficiently allocate its computing resources, when the robots send requests asking for computing service. This paper proposes a resource allocation scheme based on reinforcement learning (RL), which can make the cloud to decide whether a request should be accepted and how many resources are supposed to be allocated. The scheme realizes an autonomous management of computing resources through online learning, reduces human participation in scheme planning, and improves the overall utility of the system in the long run. Numerical results demonstrate that the proposed RL-based computing resource allocation scheme has better performances than the greedy allocation scheme.

C-MAS: The Cloud Mutual Authentication Scheme

A cloud mutual authentication scheme (C-MAS) is proposed to solve the problem of authentication between user and cloud computing server. Trusted computing technology and traditional smart card methods are used in cloud computing service platform. The scheme completes the authentication of both sides in cloud computing, generates the session key according consulting, at the same time, verifies the credibility of cloud service platform. Analysis shows that our scheme can resist various kinds of possible attacks, so it is therefore more secure than other schemes. And the computing time meet the requirements of cloud computing environment.

An Adaptive Load Balancing Method of Adjustment Based on Cloud Computing Resources

By combining LVS cluster architecture and could computing technology, system architecture of the cloud computing service platform is proposed. Cloud computing technology is suitable to support large-scale applications with flash crowds by support elastic amounts of bandwidth and storage resource etc. But traditional algorithms of load balancing provided by LVS are unsuitable for the proposed service platform, because these algorithms are designed for static server resource provided by traditional cluster technology. Taking both the overall utilization rate of server resources and the active connections of the server into counter, an adaptive adjustable load balancing algorithms (Least Comprehensive Utilization and Connection Scheduling algorithm, called LUCU) is proposed in this paper. According the utilization of cloud resource and the users demand, automatic switching between Round Robin (RR) algorithm and LUCU algorithm is achieved. When the cloud capacities are not able to meet the instantaneous demands, LUCU is chosen instead of RR. The proposed platform and algorithm are verified and evaluated using large-scare simulation experiments. The test results show that the equilibrium load is nearly achieved by adopting the proposed algorithms.