Cloud Service Platform Research Articles

Retracted: Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things.

[This retracts the article DOI: 10.1155/2022/3153454.].

Testing Serverless Applications with AWS Lambda: An Automatic Move to Serverless Architectures

In serverless computing, servers' computing resources are distributed dynamically by the cloud service provider. Consumers are charged based on the usage of resources, not on prepurchase computing capability. Programming models, abstractions, and platforms for cloud services and technologies need to evolve. This paper aims to provide large scalability and low configuration costs for cloud applications. This paper explores testing strategies for a system that allows users to request rides on unicorns from the Wild Rydes fleet. The proposed application to build, deploy and tested, for serverless services developed for the Amazon Web Services cloud platform. The results have been obtained for the parameters Duration, Error count & success rate, Call, Throttle, Total concurrent execution and compare with the existing work. The results have a less duration, with very high success rate with zero error. This paper will help users to help in state of art transportation service so that people can travel faster and easier.

Retraction Note: Research on process customization technology for intelligent transportation cloud service platform

Retraction Note: Research on process customization technology for intelligent transportation cloud service platform

Comparative Analysis of Server and Serverless Cloud Computing Platforms Vladyslav Kotyk, Yevhenii Vavruk

Cloud computing is emerging as a powerful computing paradigm for the efficient use of resources. However, decisions to move to cloud computing always remain risky from the customer's point of view, considering the benefits they get from it. Existing research on cloud computing is more focused on technical aspects such as security, quality, efficiency, etc. However, research on the implementation of cloud computing is at an early stage. Thus, in this article, an attempt is made to create a model for cost analysis and advantages for deciding on the application of cloud computing. It takes into account various organizational parameters, designing server and serverless architectures using Microsoft Azure Portal cloud platform services and policies of this organization. Also, it makes a comparative characterization of these services according to power and price criteria. A comparative description of these services according to capacity and price criteria is also given. It shows the structure of the test tool for assessment. Evaluation parameters and metrics are defined. In addition, this article contains information about approaches to evaluating cloud platforms according to various criteria that are most important for a developer.

Research on Digital Campus Construction of Lyuliang University Based on Luliang Big Data

On the basis of the existing data processing and analysis capabilities of Lyuliang College, the digital campus construction scheme of Lyuliang College is reasonably designed from six aspects: information public support environment, green and energy-saving data center, cloud service platform, big data standard system, big data acquisition system, big data processing and analysis system, so as to provide information services for school teaching, scientific research and management, and promote the optimization of teaching effects. At the same time, promote the deep integration of information technology and education and teaching.

The Dissemination Strategy of an Urban Smart Medical Tourism Image by Big Data Analysis Technology.

The advanced level of medical care is closely related to the development and popularity of a city, and it will also drive the development of tourism. The smart urban medical system based on big data analysis technology can greatly facilitate people's lives and increase the flow of people in the city, which is of great significance to the city's tourism image dissemination and branding. The medical system, with eight layers of architecture including access, medical cloud service governance, the medical cloud service resource, the platform's public service, the platform's runtime service, infrastructure, and the overall security and monitoring system of the platform, is designed based on big data analysis technology. Chengdu city is taken as an example based on big data analysis technology to position the dissemination of an urban tourism image. Quantitative analysis and questionnaire methods are used to study the effect of urban smart medical system measurement and tourism image communication positioning based on big data analysis technology. The results show that the smart medical cloud service platform of the urban smart medical system, as a public information service system, supports users in obtaining medical services through various terminal devices without geographical restrictions. The smart medical cloud realizes service aggregation and data sharing compared to the traditional isolated medical service system. Cloud computing has been used as the technical basis, making the scalability and reliability of the system have unprecedented improvements. This paper discusses how to effectively absorb, understand, and use tools in the big data environment, extract information from data, find effective information, make image communication activities accurate, reduce the cost, and improve the efficiency of city image communication. The research shows that big data analysis technology improves patients' medical experience, improves medical efficiency, and alleviates urban medical resource allocation to a certain extent. This technology improves people's satisfaction with the dissemination of urban tourism images, makes urban tourism image dissemination activities accurate, reduces the cost of urban tourism image dissemination, and improves the efficiency of urban tourism image dissemination. The combination of the two can provide a reference for developing urban smart medical care and disseminating a tourism image.

Research on home-school linkage mechanism in Chinese universities based on cloud service platform design

In the development of modern university education reform, the design of cloud service platform is proposed in the field of education, promoting the brand new management environment, so that teachers and students can enjoy the diversified services through the network platform, change the traditional campus life mode, and truly realize the win-win situation between platform merchants and ordinary users. According to the cumulative experience of Chinese college education reform in recent years, the linkage mechanism of colleges and universities is the main issue of practical inquiry, and plays an important role in the overall implementation of modern education work. Therefore, on the basis of understanding the research status of home-school linkage mechanism construction in Chinese colleges and universities, this paper defines the integrated development strategy of home-school linkage mechanism in colleges and universities in the future according to the design architecture and basic functions of cloud service platform, so as to build a better college education management system.

Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly.

Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform

Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMiDvne</i> . Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMiDvne</i> then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMiDvne</i> outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMiDvne</i> improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45, 40.45, and 299.03 percent, respectively, and reduces the average rate of cost by up to 64.16 percent. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMiDvne</i> to efficiently reduce communication costs by up to 45.93 and 63.43 percent for download and upload, respectively.

Authentication methods for internet of vehicles based on trusted connection architecture

This paper proposes an authentication protocol based on a trusted connection architecture to manage the security and reliability of the cloud service environment during the communication process, improve the trust of the cloud service platform toward vehicles, and ensure that vehicle terminals have reliable access to cloud services. Compared with prior Internet of Vehicle (IoV) authentication schemes, our scheme is the first to include platform identification in the authentication process. Based on the characteristics of the trusted connection architecture, the components that constitute the platform can be assessed for security by verifying the vehicle platform identity and platform integrity metrics, thereby eliminating internal threats. In addition, the protocol proposes an authentication scheme for the IoV environment, in which the trusted authority only needs to generate the user's partial key based on the identity, thereby avoiding the key escrow problem common to identity-based cryptosystems. Finally, the scheme is proven to be highly secure using various approaches, such as Syverson-Van Oorschot (SVO) logical analysis, simulated authentication via automated validation of internet security protocols and applications (AVISPA), and informal security analysis. In the identity authentication step, our method has low computation and communication overhead when compared with other schemes according to the performance analysis results.

MEC-Enabled Fine-Grained Task Offloading for UAV Networks in Urban Environments

In recent years, with the continuous development of information technology, the amount of data generated and hosted by cloud service platforms in urban environments is unprecedented. Mobile edge computing (MEC) is combined with UAV networks to better realize the ability to provide nearby services to a large number of terminal devices in cities. Unmanned aerial vehicles (UAVs) are highly maneuverable and inexpensive and are good carriers for carrying MEC platforms. In UAV edge networks, we usually face the problem of fine-grained task offloading based on relevant features of urban environments. We need to address high energy consumption and task processing delays to help achieve urban sustainability goals. Therefore, we combine the software definition network (SDN) technology and, on this basis, we propose two task offloading strategies based on an improved EFO intelligent algorithm for different user scales. At the same time, we run the proposed offloading system in the UAV sensor. The experiment shows that, compared with the traditional strategy, the unloading efficiency of the proposed method can be improved by about 10%.

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.

A personalized recommendation method under the cloud platform based on users’ long-term preferences and instant interests

Rich consumer online text data are embedded in the cloud platform. Using new technologies has become a central issue for acquiring consumer preference, analyzing consumer demand, and performing personalized recommendation services. In order to recommend the cloud platform services efficiently and accurately, this paper proposes a personalized recommendation model referred to as Residual bi-directional Recurrent Neural Network with Dual Attentive mechanism (BiRDA) for the service recommend to cloud platforms, by combining users’ long-term preferences with instant interest. The proposed recommender prototype is summarized as follows. (1) Analyzing the relationship between long-term preferences and instant interests based on co-opetition theory. (2) Extracting users’ online text data from the cloud platform. (3) Deriving the product attribute words of user preference using an analysis of online text data. (4) Product attribute words are transformed into the form of word vectors. (5) The word vector is input into the Residual bi-directional Recurrent Neural Network (Res-BiRNN) to make the prediction. On the one hand, the long-term preference is expressed by the user's field of expertise (i.e., answer content). On the other hand, the even interest is expressed by the user's changing interest (i.e., question data). (6) Assigning different weights to long-term preferences and instant interest using the dual attention mechanism to output predictions. (7) Generating recommendation lists for users based on the predicted values. Accordingly, BiRDA is compared with five state-of-the-art recommendation methods (i.e., DREAM, BINN, SHAN, Caser, and DeepMove), as well as six variants of the BiRDA model, Using users’ Q&A datasets from NiorcngeCDS cloud platform, XMAKE cloud platform, and Asksubarme cloud platform as examples. The experiments show that the proposed method is more efficient and accurate than the other models. Therefore, the study offers some important insights into allowing a large number of resources under the cloud platform to be fully utilized and provides a novel idea for the construction of the cloud platform front-end.

Research on the Intelligent Cloud Animal Husbandry Platform

Aiming at the problem that the traditional talent training mode is difficult to cultivate the intelligent animal husbandry compound talents needed by the implementation of the rural revitalization strategy, a cloud animal husbandry platform is designed to cultivate “Understand Agriculture, devote to Rural Community and Be Sincere to Farmers” compound talents. The platform adopts the two-wing multilevel architecture design of the concept of the cloud side, which is composed of three parts: the data center platform, resource sharing platform, and business cloud service platform. The data center platform is responsible for the collection, processing, integration, and asset management of the four-dimensional real data and provides computing, storage, and data asset services for the cloud livestock platform. The resource sharing platform provides sharing services from resources and development kits to carriers. Livestock cloud service platform is to carry out the integration of industry, education, and science and education, the design and implementation of cloud teaching, cloud scientific research, and cloud breeding business cloud services. Through its practical application in many universities and enterprises, the platform is verified to be safe, effective, and reliable in teaching, scientific research, and social services and has both operational and promotional value.

Application of Smart Wearable Fitness Equipment and Smart Health Management Based on the Improved Algorithm.

IoT technology has scientific, advanced, and practical characteristics in wearable fitness equipment and artificial intelligence health management. On this basis, this paper discusses the intelligent health management system based on IoT technology. By building an IoT health management system, the design principles and algorithm design of wearable fitness equipment are introduced around the device, and various noises that affect the accuracy of the equipment and how to better filter out various noises are also analyzed. The authors selected cloud service platforms and also investigated and analyzed the application prospects of wearable fitness equipment in the community. From the perspective of real-time monitoring, scientific management, and intelligent decision-making, we ensure high-quality, standardized, scientific, efficient, and accurate wearable fitness equipment applications and intelligent health management. In addition to simply viewing IoT technology as an information dissemination platform, IoT technology can also expand many other functions. According to the survey results of this article, 90% of people will use the APP with the function of sports and fitness in social media. One of the most concerned issues in the current society is the application of social media in national fitness activities.

RETRACTED: Optimizing Scalability and Availability of Cloud Based Software Services Using Modified Scale Rate Limiting Algorithm

Testing and measuring the performance and scalability of cloud-based software services is essential for the further development and improvement of cloud computing. The criteria for the performance of cloud-based software services are intertwined with scalability, flexibility, and efficiency. Especially in the context of quickly growing the quantity of service delivery, the performance evaluation and testing of cloud-based software services is crucial to support the Service Level Agreement (SLA) compliant quality of delivery of these services. In this work, we use the Token bucket method, a scale rate-limiting algorithmic technique, to maximize the efficiency of cloud-based service performance. Increased availability and scalability in distributed systems has been a major obstacle for a long time. For cloud computing businesses to keep their customers happy, keep their trust, and keep their money, they need to make their services easy to use. The Spring Cloud-based software service uses Zuul as its gateway, therefore it must execute rate-limiting and guarantee service stability in the face of rapid growth. Inability to guarantee availability of essential services while using token bucket rate restriction. This research created a method for protecting against token overuse by using a URI configuration file in tandem with the Zuul gateway to potentially filter requests before receiving tokens. The token-bucket rate-limiting technique was created to perform the traffic-limiting function and guarantee the availability of the cloud platform's services. Measuring both scalability and availability helps illustrate the quality of service provided to customers. The performance of cloud-based software services is evaluated with regards to scalability using elasticity measurements. A growth in demand for cloud-based software services is a potential outcome of ongoing research and development in the field of cloud computing.

Conformance-oriented Predictive Process Monitoring in BPaaS Based on Combination of Neural Networks

As a new cloud service for delivering complex business applications, Business Process as a Service (BPaaS) is another challenge faced by cloud service platforms recently. To effectively reduce the security risk caused by business process execution load in BPaaS, it is necessary to detect the non-compliant process executions (instances) from tenants in advance by checking and monitoring the conformance of the executing process instances in real-time. However, the vast majority of existing conformance checking techniques can only be applied to the process instances that have been executed completely offline and only focus on the conformance from the single control-flow perspective. We develop an extensible multi-perspective conformance measurement method to address these issues first and then investigate the predictive conformance monitoring approach by automatically constructing an online multi-perspective conformance prediction model based on deep learning techniques. In addition, to capture more decisive features in the model from both local information and long-distance dependency within an executed process instance, we propose an approach, called CNN-BiGRU, by combining Convolutional Neural Network (CNN) with a variant and enhancement of Recurrent Neural Network (RNN). Extensive experiments on two data sets demonstrate the effectiveness and efficiency of the proposed CNN-BiGRU.

Research on the Significance of Big Data and Artificial Intelligence Technology to Enterprise Business Management

With the continuous development of information technology, China has officially entered into the era of big data, the enterprise business management has realized intelligent business management based on information technology, and the arrival of the era of big data has to some extent reduced its management and risk costs and improved its work efficiency and economic benefits, but at the same time, it also faces a series of challenges, such as with the continuous expansion of business scope. As the business scope continues to expand, its current management model cannot meet the development needs. The integration of artificial intelligence technology and enterprise business management has become the main trend of future development. The application of big data technology to corporate business management enables the production of products and the creation of product value in the direction of socialization, increases the mutual influence between companies and consumers, and enhances the fairness, openness, and justice between corporate development and consumer activities. In this paper, after a detailed understanding of the current situation of the combination of artificial intelligence technology and enterprise business management, we focus on the application path of artificial intelligence technology in enterprise business management and finally elaborate the future development direction of artificial intelligence technology in the field of enterprise business management, hoping to further deepen the relevant personnel’s understanding of artificial intelligence. Analyzing the current situation of business management development in China, this paper proposes applying big data technology and artificial intelligence technology to enterprise business management work and proposes a new model to bring new development opportunities and development directions for enterprise business management work. The big data technology proposed in this paper is based on cloud platform services, while artificial intelligence based on neural network is embedded in the cloud platform for enterprise business management services.

Tenant-Centric Attribute Semantic Access Control Policy Model for the Cloud Service Platform

In the open Internet environment, there is a cross-platform access control problem that when a tenant needs to access the business resources of other collaborative platforms through the cloud service platform, the cloud service platform only supports the tenant to access the business resources within the platform. When tenants need to access business resources through the cloud service platform, the authorization method of the cloud service platform is static and the authorization granularity is coarse-grained, so dynamic fine-grained authorization is not supported. To solve the above problems, based on ABAC, this paper proposes a tenant-centric attribute semantic access control policy model for cloud service platforms. The model and its application framework can automatically evaluate whether it has cloud service platform or cross-platform access control rights according to the change of the tenant’s attributes so as to determine whether it can obtain the corresponding business resources. Through a practical case analysis, we prove that the application of ASACPM and its application framework to the cloud service platform have good flexibility, scalability, and practicability. In addition, we design some experimental scenarios to verify that the performance of ASACPM and its application framework meet our expectations and have good reliability, validity, and rationality.

Rancang Bangun Pendeteksi Kebakaran dan Monitoring Berbasis IoT dengan Microcontroller NodeMCU

In general, every house or shop has the possibility of experiencing a fire, which is caused by many factors. We need a fire detection tool that will notify the building owner so that it can be handled early and effectively. In this research, an Internet of Things Based Fire Detector Design will be made using the NodeMCU Microcontroller to detect fire and smoke to determine the occurrence of a fire using an IR Flame Sensor[1] and MQ-2 Sensor[2], [4], where the input data comes from reading the sensor value and the data is sent to the cloud service platform. thinger.io and the data is displayed in live monitoring. From the design made, the results obtained from the IoT-Based Fire Detector Design Using the NodeMCU Microcontroller to detect fire and smoke to determine the occurrence of fires.