Cloud Computing Model Research Articles

Research on the Application of Cloud Computing in Enterprise Credit Rating System

At present, in the context of the "Internet+" era, cloud computing has become the basic equipment in the era of digital economy, and the increasing improvement of the big data ecosystem has laid an important foundation for the development of cloud computing, and cloud computing has also catalyzed the rapid development of big data in the application field. This paper is based on the application of cloud computing to enterprise credit rating system, that is, the collection of enterprise information and behavior information through a distributed platform; The collected data is modeled and verified through the big data mining platform; Provide cloud desktops for credit rating applications through virtualization technology; Through the exploration of credit rating theory and credit rating model development. At the same time, combined with the related technology and theoretical knowledge of cloud computing, a credit rating solution model for cloud computing is found. The cloud computing application of the credit rating system was initially realized through VMware software.

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

Data storage query and traceability method of electronic certificate based on cloud computing and blockchain

In the age of big data, electronic data has developed rapidly and gradually replaced traditional paper documents. In daily life, all kinds of data are saved in the form of electronic documents. In this regard, people have strengthened the development of electronic depository system. Electronic storage refers to the storage of actual events in the form of electronic data through information technology to prove the time and content of events. Its application scenarios are very extensive such as electronic contracts, online transactions and intellectual property rights. However, due to the vulnerability of electronic data, the existing electronic data depository system has certain security risks, and its content is very easy to be tampered with and destroyed, resulting in the loss of depository information. Due to the complexity of the operation of the existing electronic data depository system, some users are likely to reduce the authenticity of the depository information due to the non-standard operation. In order to solve the problems existing in the current electronic data storage system, this paper designed an electronic data storage system based on cloud computing and blockchain technology. The data storage of cloud computing and blockchain was decentralized, and its content cannot be tampered with. It can effectively ensure the integrity and security of electronic information, which is more suitable for the needs of electronic storage scenarios. This paper first introduced the development of electronic data depository system and cloud computing, and optimized the electronic data depository system through the task scheduling model of cloud computing. Finally, the feasibility of the system was verified through experiments. The data showed that the functional efficiency of the system in the electronic data sampling point storage function, the upload of documents to be stored, the download of stored documents, the view of stored information function and the file storage and certificate comparison verification function has reached 0.843, 0.821, 0.798, 0.862 and 0.812 respectively. The final function indexes of each function of the traditional electronic data depository system were 0.619, 0.594, 0.618, 0.597 and 0.622 respectively. This data shows that the electronic data storage system based on cloud computing and blockchain modeling can effectively manage electronic data and facilitate relevant personnel to verify electronic data.

Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment

This theoretical research in fuzzy correlation analysis integrates data uncertainty analysis by measuring the strength of the linear relationship restricted to two fuzzy sets. As the relevant contribution to this research area, this paper presents the axiomatic definition of the n-dimensional generalized fuzzy correlation coefficient (nGCC), assigning to n input fuzzy sets an output value in the interval [−1,1]. Thus, the properties of general overlap functions and fuzzy negations are studied, discussing binary non-normed restricted dissimilarity functions, and the n-dimensional non-normed conjunctive functions. This study provides new methods for the n-dimensional generalized fuzzy correlation coefficient analysis, regarding their applications in solving multi-criteria and decision-making problems founded on fuzzy logic extensions. The n-dimensional non-normed conjunctive aggregation functions are also introduced, as range domain extensions from [0,1] to [−1.1], covering the interpretation of negative to positive variable associations. The proposal correlation analysis promotes a better evaluation and data selection even when more than one algorithm is applied to evaluate the reduction methods in the defuzzification process based on Interval-valued Fuzzy Logic. We also investigate the relevance to determine a reliable result from the fuzzy inference system based on the nGCC methodology. This correlation methodology applied to the Interval Fuzzy Load Balancing for Cloud Computing (Int-FLBCC) model contributes as a flexible approach for virtual machines dynamic consolidation enabling improvements in resource usage and power efficiency, improving the computational system’s energy efficiency. So, the nGCC methodology extends the Int-FLBCC model by adding other degrees of reliability to the results obtained with diverse evaluations through n-dimensional generalized fuzzy correlation coefficient expressions, exploring average aggregations as arithmetic and exponential means and the median operator.

Performance Analysis of Scheduling Algorithms on Fog Computing using YAFS

A smart device that has seen more development is the Internet of Things (IoT). An IoT system implementation requires a device that can accept and handle various sorts of data. Fog Computing is a solution to the issue since the IoT demands a device that can provide Real-Time. Certainly, load balancing involves scheduling the IoT devices and data used. Because Cloud and Fog Computing models enable data growth management and deployment planning, which necessitate a quicker response from platforms and applications, processing power scheduling is essential. The purpose of this study is to evaluate the performance of effective scheduling algorithms that adhere to these computing models platform requirements. The scheduling algorithm that can produce the lowest Processing Time and the resulting Time Efficiency is more efficient can be called the best scheduling algorithm. In this research, the author analyzes the performance of scheduling algorithms in the form of Round Robin and Priority Scheduling on Fog Computing. In this research, testing was carried out by creating a scenario of the effect of increasing the number of Fog Nodes and Devices used. The average result of scenario testing obtained for processing time for Round Robin is lower, and the highest Time Efficiency for Round Robin over Priority Scheduling is 11%. With these test results, the Round Robin scheduling algorithm has a simpler level of complexity. So, it can be concluded that Round Robin belongs to the category of the best scheduling algorithm in this case.

A Technique for Generating Preliminary Satellite Data to Evaluate SUHI Using Cloud Computing: A Case Study in Moscow, Russia

The expansion of construction zones, transportation, and utilities for industry and high-tech areas due to human activities has caused the deterioration of the natural ecological environment. As cities face problems related to the surface urban heat island (SUHI) effect and environmental pollution, there is an urgent need to develop new methods for the ecological–microclimatic assessment and structural–functional planning of urban areas. The main goal of this study was to demonstrate the evolution of the surface urban heat island (SUHI) effect in Moscow over a long period and to determine the interaction between SUHIs and urban pollution islands (UPIs) using a geospatial analysis platform while optimizing vegetation classification with machine learning. Additionally, we are creating a digital database for modeling the sustainability of cities on the GEE platform using cloud computing. This study used cloud computing and remote sensing image analysis platforms for a 17-year temporal-series ecological–microclimatic assessment, which provided a sequence of values describing the ongoing process of changes in the ecological conditions of Moscow over time. Combining machine learning with the random forest algorithm (RF) improved vegetation classification accuracy while reducing computation time. The study findings demonstrated how the SUHI affected Moscow’s territory and showed the urban areas significantly impacted by this phenomenon. The locations of surface urban heat islands in Moscow and areas affected by SUHI and UPI were identified using numerical modeling of the urban thermal field variance index (UTFVI). From the findings, we identified the need to develop a new method for obtaining geospatial data for assessing the interaction between UPIs and SUHIs using cloud computing and mathematical data models.

Analysis of the Efficiency of the Simulation Model of Cloud Computing in Case of Multiple Server Failures

Currently, the use of distributed (cloud) resources is becoming more and more widespread. Despite the high reliability of equipment used in call centers, failures do occur. And if long-term storage systems have high reliability due to multiple duplication, then the failure of the server on which the task is performed in specialized computing systems (SCS) can correspond to the failure of the entire system. In order to eliminate it, a simulation model of a specialized computing system built in the Simulink (SimEvent) environment, classified in the queuing theory as G/G/n/, is investigated. It is characterized by many input streams with an infinite queue, has two feedbacks, reflecting reprocessing situations in case of a server failure or no solution at the first service attempt. The system architecture is focused on the parallel execution of a certain class of tasks, divided into data-independent sub-tasks. The main criterion for the functioning of such a system is the completion of all received tasks in a time not exceeding the deadline for their implementation. The purpose of the work, the results of which are presented in the article, is to study the reliability of the functioning of such a system for various values of the probability of server failure. Since the performance and efficiency of such a system has already been studied in previous works, the main direction of research will be focused on the impact of server failures on overall productivity and efficiency under various laws of input flows.

The Effectiveness of Different Cloud Computing Models on Organizational Performance: An Analytical Study

The Effectiveness of Different Cloud Computing Models on Organizational Performance: An Analytical Study

A Bio inspired Approach for Load Balancing in Container as a Service Cloud Computing Model

A Bio inspired Approach for Load Balancing in Container as a Service Cloud Computing Model

Assessment of water demands for irrigation using energy balance and satellite data fusion models in cloud computing: A study in the Brazilian semiarid region

Assessment of water demands for irrigation using energy balance and satellite data fusion models in cloud computing: A study in the Brazilian semiarid region

Investing Data Flow Issue by using Rayleigh Model in Cloud Computing

Programming groups benefit significantly from programming error forecasting, which also advances current achievement. Programming imperfection expectation has been the subject of experimental studies on both inside- and outside-project deformity forecast. However, current analyses dont appear to be able to provide a method for estimating the number of flaws in a soon-to-be-released product. This paper describes such an approach and determines the relationship between each indicator variable and the total number of defects using indicator variables obtained from the deformity speed increase, namely the imperfection thickness, deformity speed, and imperfection presentation time. We describe how a coordinated AI strategy was used in light of relapse models created using these indicator criteria. 3 distinct datasets with 228 occurrences were taken from the Kaggle store and subjected to analysis. The modified R-square for the relapse model developed as a component of the usual deformity speed was 98.6%, with a p-value of 0.001 being achieved. With a connection value of 0.98, the average deformity speed is clearly correlated with the number of flaws. As a result, it is demonstrated how this process might provide a framework for programmer testing in order to increase the viability of programming advancement activities.

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks.

Integrating the EGC, EF, and ECS Trio Approaches to Ensure Security and Load Balancing in the Cloud

According to data protection studies, "Distributed Denial-of-Service (DDoS)" threats have cost governments and businesses throughout the globe a large number of financial resources. Despite this, the existing practices fall short of the standards set by "Cloud Computing (CC)" monitoring technology. They ignore the "Intrusion Detection Systems (IDS)" techniques, which take advantage of the CC's multiple tenants and elasticity qualities, and also the hardware limitations. Attackers are finding increasing ways to effectively exploit them because of their rising complexity. DDoS assaults of this scale have never been observed online before 2018. As online services get more popular, so does the amount of DDoS assaults and malevolent hackers leading to terrible. Numerous IDS for DDoS are already in place to address this problem. One of the most challenging aspects of virtualization is establishing a "Trust Model (TM)" between the many "Virtual Machines (VMs)". The lack of a standard formulation for generating a TM would be the primary reason. As a consequence, the integrity of every VM might not have been recognized by an independent trust, which might lead to a decrease in trust value. In this research for TM creation, "Enhanced Graph Based Clustering (EGC)" is proposed, while "Enhanced Fuzzy (EF)" is used for detecting attacks, and the "Enhanced Cuckoo Search (ECS)" method is used to find the ideal "Load Balancing (LB)" distribution. By creating a new TM, the proposed (EGC-EF-ECS) system strengthens trust value. To expand the CC model's stability, it optimizes attacker recognition percentage and makes better use of resources by restricting each VM's processing, bandwidth, and storage requirements. The proposed EGC-EF-ECS outperformed the previously used BPA-SAB, and DCRI-RI approaches in terms of the "Intrusion-Detection-Rate (IDR)", "Load-Balancing-Efficiency (LBE)", and "Data-Accessing-Time (DAT)" evaluation metrics.

Cloud Computing Model for E-Commerce in Saudi Arabia

The impact of cloud computing can be seen in several industries, including e-learning, healthcare and education. It offers customer service lives with high productivity and minimal cost, producing a high financial value. In contrast to developed countries, the use of cloud computing is still relatively new and is not widely accepted in developing countries like Saudi Arabia (SA). In essence, implementing a modern approach to the cloud is dependent on a number of factors that may affect the Saudi Arabian government's preference to do so. Therefore, it is critical to comprehend why some companies are better equipped than others to transition to cloud computing in order to support the application of cloud knowledge. Accordingly, this article aims to present a cloud computing model for E-commerce in SA that integrates the E-commerce as a service business and cloud computing. The proposed model also offers several internal services, including security services, Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Hardware as a Service (HaaS), Software as a Service (SaaS) and Data as a Service (DaaS). Throughout this, it is anticipated that the suggested model will work with reliable management, server and business modules in addition to sources of hardware and software.

Multi-Job Scheduling and Optimization Model for Cloud Computing

Multi-Job Scheduling and Optimization Model for Cloud Computing

An Efficient Task Offloading Approach Based on Multi-Objective Evolutionary Algorithm in Cloud-Edge Collaborative Environment

Cloud-edge collaboration can overcome the shortcomings of high latency of the centralized model of cloud computing and insufficient computing power of edge computing, and better meet the needs of various application scenarios, which has become a research hotspot in recent years. The 6 G network is driven by the deep integration of network and computing, which will further accelerate the collaborative and integrated development of the cloud datacenter, edge servers, end devices, and application. This paper considers the combination of edge computing and cloud computing, and studies the problem of task offloading with the optimization objectives of energy consumption, delay, and multi-node load balancing under an “End-Edge-Cloud” collaborative architecture for 6G network. We regard the problem of task offloading as a multi-objective optimization problem. By improving AR-MOEA, a task offloading algorithm based on the “End-Edge-Cloud” architecture (TO-EEC) is proposed. It is verified through experiments that TO-EEC has a fast convergence speed. Compared with other similar offloading algorithms, TO-EEC has significant optimization effects on energy consumption, delay and multi-node load variance under multiple constraints.

Complexity and Monitoring of Economic Operations Using a Game-Theoretic Model for Cloud Computing

In this study, a model is presented for allocating cloud computing resources based on economic considerations using tools from game theory. The model, called the Non-Cooperative Game Resource Allocation Algorithm (NCGRAA), is designed to achieve the optimum stage in cloud computing. In addition, the Bargaining Game Resource Allocation Algorithm (BGRAA) is introduced to the existing system to develop the billing process within the constraints of availability and fairness. This system-based algorithm implements methods for converging on and improving the Nash Equilibrium and Nash Bargaining solutions. While the Nash equilibrium helps to develop decision-making concepts with game theory, one of its main goals is to achieve the desired outcome and avoid deviation from the working stage. Nash Bargaining is a unique solution that occurs between two parties and takes into account the process of bargaining to provide a fair solution that is scale invariant and independent. In recent years, cloud computing has become a popular way to manage computing services and enable producers and consumers to interact. This process allows users to obtain goods at an affordable cost from sellers according to their expectations. This research investigates the economic operation monitoring of cloud computing using the gaming theory model. A Static Negotiation Analysis Method with a Bargaining Process (SNAM-BP) for a dynamic conceptual framework is presented to display the weighted relationship between primary issues and keywords used to evaluate the potential partnership of each country.

Security and Privacy Issues in IoT-Based Big Data Cloud Systems in a Digital Twin Scenario

Due to its unique type of services, Cloud Computing could operate as a “base technology” for other technologies; this attracts researchers to develop sustainable Cloud systems. It is a new generation of services that offers an opportunity for users to access and manage their information, applications, and data regardless of place and time. Nevertheless, there is a type of service that can include large amounts of data, called Big Data, and it consists of the rapid use of the Internet of Things (IoT) to produce large data sets. In this work, initially, we present Cloud Computing (CC) and Big Data (BD) exported from IoT, focusing on the security and management challenges of both. Notably, we combine the two aforementioned technologies to examine their related characteristics and discover new perspectives and opportunities for their integration and to achieve a sustainable environment called a Digital Twin scenario. Subsequently, we present how Cloud Computing contributes to IoT-based Big Data, aiming to fill a scientific gap in the sector of their integration regarding security and privacy. Finally, we additionally survey the security challenges of the integrated model of BD and CC and then propose a novel security algorithm for sustainable Cloud systems in a Digital Twin scenario. The experimental results presented are based on the use of the encryption algorithms AES, RC5, and RSA, and our proposed model extends the advances of CC and IoT-based BD, offering a highly novel and scalable service platform to achieve better privacy and security services.

Cloud computing for information management and use in the 21st century library

Librarians are applying different platforms for user services and attaining exceeding satisfaction for information management and use in this Web based era. Cloud computing presents an advantage for libraries. It is providing several opportunities for libraries to link their services, use and manage information on the cloud. Cloud computing is the new type of service provided on the Internet, which has exclusively changed the way computers are used irrespective of geographical barriers to manage and use information. The paper examines cloud computing as a tool for information management and use in the 21st century library. The discourse is about the concept of cloud computing, models of cloud computing and the service, which include IaaS, PaaS and SaaS. The paper also discusses cloud. The paper also discusses cloud deployment storage models in private, community, public, hybrid and personal cloud. Information management concept are highlighted, alongside applications of cloud computing for information management and usage in the 21st century libraries. Benefits of using cloud computing for information management in the 21st century libraries including the risks associated with cloud computing for information management and use in the 21st century libraries. Concluding, the discourse opines that today libraries are shifting to cloud computing technology to process data correctly and quickly. Cloud based services are helping libraries by creating digital libraries, social networking and enhance information sharing. Though, problems regarding data security, privacy, reliability and legal rights have not been fully taken care of. All information personnel must be aware of these issues before choosing cloud based services. Despite these issues, cloud based technology is an incredible development that can improve data management and usage in libraries and offer dependable, rapid services to its users. The study recommends that base on the importance and relevance of cloud computing technology, that it should be applied in library services to manage and use information must especially university libraries in Nigeria.

Организация конфиденциальных запросов к облаку

The article examines the well-known cryptographic problem of obtaining data from a database by a client so that no one with access to the server except the client himself could obtain information about this request. This problem known as PIR (Private Information Retrieval) was formulated in 1995 by Chor, Goldreich, Kushilevitz and Sudan in the information-theoretic setting. A model of cloud computing is proposed. It includes a cloud, an authentication center, a user, clients, trusted dealer, an active adversary executing the protocol in the cloud. The attacking side has the ability to create fake clients to generate an unlimited number of requests. An algorithm for the organization and database distribution on the cloud and an algorithm for obtaining the required bit were proposed. An injective transformation of bit numbers represented in the l-ary number system by words of length d into words without repeating digits of the same length with an alphabet of 𝒍̂ digits is used, i.e. a transformation {𝟎, ..., 𝒍 − 𝟏}^𝒅 → {𝟎, ..., 𝒍̂ − 𝟏}^𝒅 was constructed. This transformation reduces the probability of disclosure of the requested bit number. The communication complexity and probability of revealing required bit were estimated, taking into account the performed transformation.