Cloud Computing Model Research Articles

Machine learning with big data analytics for cloud security

The amount of data generated and transmitted more quickly, particularly with the demand for action in real-time, has greatly increased with the growing number of internet-connected devices. With the rising diversity of data and need for data integrity, it is more challenging to accomplish this processing on time. However, cloud and edge computing pose security problems and time delays, as computing models are being applied rapidly. Hence in this paper, a Machine Learning-Assisted Cloud Computing Model (ML-CCM) with big data analytics has been proposed to increase security and improve data transmission rates. The simplest approach for storing large volumes of data is cloud storage. Big data can manage or store large amounts of distributed data in clouds. The ML algorithms analyze supervised and unsupervised training used to solve cloud protection problems. The experimental results show that ML-CCM has a data transmission rate of 96.4%, effective data management of 94.3%, computational time of 35.2%, accuracy of 91.7%, and performance of 95.2%.

Cloud-based sustainability assessment (CSA) system for automating the sustainability decision-making process of built assets

Digital tools help facilitate the implementation of sustainability practices in the built environment in the era of rapid urban developments. However, the extant literature revealed salient gaps in the use of cloud-based systems and digital tools to evaluate buildings' sustainability performance. Consequently, the current study aims to develop and implement a cloud-based sustainability assessment (CSA) system to evaluate and compare the sustainability performance of buildings based on the Building Sustainability Assessment Method (BSAM) scheme green rating system. A design science research (DSR) approach was adopted in designing, developing, and validating the CSA system. The developed CSA system is based on the SaaS model of cloud computing. The methods evaluation validation of the CSA system when compared with other systems revealed that the developed CSA system would result in higher time and learning efficiency for the users. The findings from the case study validation of the CSA system, using four building projects, indicate that the developed artefact offers a better secured, automated, reliable, and value-adding tool for the building sustainability assessment process. It also eases the automated process of updating data, evaluating, and comparing building projects towards improving the overall building sustainability profile. More so, the CSA system has a great potential to assist stakeholders in their buildings’ sustainability decision-making process and enhancing sustainable development.

Task Scheduling Strategy Based on Queueing Model in Cloud Computing and Its Energy Consumption Optimization Analysis

In order to reduce the energy consumption of the cloud computing system while ensuring the response performance of the cloud users, a task scheduling strategy is proposed. In the strategy, the local processor of the mobile device continues to work, the virtual machines in a physical machine sleep synchronously, and the virtual machines in different physical machine sleep asynchronously. For the heterogeneous physical machines in cloud computing, a queueing model with a synchronous multiple vacation is established. By using the quasi-birth-death process and the matrix geometric solution, the steady-state distribution of the queueing model is given, and the expressions of the average response time of the tasks and the average power of the system are derived. The numerical results show that there is a power-performance trade-off for cloud computing when setting the assigning probability of the tasks to the local processor. Through the improved whale optimization algorithm, the task scheduling strategy is optimized with the minimum system cost.

Analysis of the Mental Health of Urban Migrant Children Based on Cloud Computing and Data Mining Algorithm Models

With the rapid development of internet technology, the amount of data generated is also increasing day by day. As a kind of distributed computing, cloud computing has been widely used in the analysis of massive data. With the development of China’s economic construction, the integration of urban and rural areas is constantly improving, and the migrant children in the city are also focused on. After moving into the city, migrant children not only face the pressure from the society but also face the pressure from life, which inevitably affects the physical and mental health of urban migrant children. The education of urban migrant children is also a focus that needs attention. How to integrate into the education environment of urbanization and adjust the learning pressure in the process of education is also worthy of our attention. Therefore, this article analyzes the current status of urban migrant children’s mental health based on cloud computing and data mining algorithm models. Based on the current research status of urban migrant children and the standards of mental health, this paper conducts a survey of middle and high school students in a certain city through questionnaires, then builds a data mining algorithm model to analyze the survey data, and explores the differences in the grades of students’ social identity and the differences in mental health between migrant children and urban children. According to the survey, most of the psychological performances of urban migrant children are very vague. At the same time, there are also some phenomena such as poor adaptability, bad mood, and inferiority complex. During the study period, there are situations such as unwilling to communicate with others, weariness, sensitivity, anxiety, and hostility. The overall incidence of the situation is relatively high in big cities, while the situation of urban children is relatively small.

Design and Realization of Webpage Operation Computer System Based on Cloud Computing

In recent years, our country’s computer industry has developed vigorously. With people’s increasing demand for computer service data processing capabilities and computer operating system portability and cross-platform requirements, traditional computers are no longer competent, and there will always be calculations. Speed cannot take into account portability and cross-platform, the embarrassing situation that computing speed cannot meet the standard for portability. And now is the time for the rapid development of the Internet industry, and the development of network speed has promoted this new model of Cloud Computing(CC). CC is a network-based shared computing resource pool service model with on-demand access and capacity expansion. It is one of the solutions for massive data storage and processing, large-scale concurrent operations and other services. In order to solve the awkward situation between portability and performance in the computer field, this paper designs a web-based computer system based on the CC model to provide private cloud and public cloud services to individual users and corporate users, encapsulate the underlying services, and implement the system. The unique system application framework provides interfaces to third-party developers while implementing the background system. Webpage operating systems try to gradually transform CC from concept to materialization. CC’s on-demand services, on-demand access, independent resource pool division, fast flexibility, and measurable access are integrated. After system performance testing and a period of online public testing, the implementation of web-based cloud operating system and system performance have been guaranteed.

Enhanced Spatial–Temporal Map-Based Video Analytic Platform and Its Local- Versus Cloud-Based Deployment with Regional 511 Camera Network

This paper explores the cloud- versus server-based deployment scenarios of an enhanced computer vision platform for potential deployment on low-resolution 511 traffic video streams. An existing computer vision algorithm based on a spatial–temporal map and designed for high-angle traffic video like that of NGSIM (Next Generation SIMulation) is enhanced for roadside CCTV traffic camera angles. Because of the lower visual angle, determining the directions, splitting vehicles from occlusions, and identifying lane changes become difficult. A motion-flow-based direction determination method, a bisection occlusion detection and splitting algorithm, and a lane-change tracking method are proposed. The model evaluation is conducted by using videos from multiple cameras from the New Jersey Department of Transportation’s 511 traffic video surveillance system. The results show promising performance in both accuracy and computational efficiency for potential large-scale cloud deployment. The cost analysis reveals that at the current pricing model of cloud computing, the cloud-based deployment is more convenient and cost-effective for an on-demand network assessment. In contrast, the dedicated-server-based deployment is more economical for long-term traffic detection deployment.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

Background: Cloud computing is characterized as the arrangement of assets or accessible administrations by the cloud service providers through web to their clients. It communicates everything as administrations over the web as per the client request, for example, the operating system, organization of equipment, storage, assets, and software. Nowadays, Intrusion Detection Systems (IDS) play a powerful role while working under the influence of the experts who act when a system is hacked or under some intrusions. Most intrusion detection frameworks are created based on the machine learning strategies. Since the datasets play a major role, this is utilized as part of the intrusion detection i.e., Knowledge Discovery in Database (KDD). Methods: In this paper, the intruded data was detected and classified utilizing Machine Learning (ML) with MapReduce model. The primary objective of the Hadoop MapReduce model is to reduce the extent of database ideal weight that was decided for reducer model and second stage by utilizing Decision Tree (DT) classifier in data detection. This DT classifier utilizes an appropriate classifier to decide the class labels for non-homogeneous leaf nodes. The decision tree fragment provided a coarse section profile while the leaf level classifier yielded the data about the qualities that influence the label inside a portion. Results: From the proposed results, the accuracy for detection was 96.21% in comparison with the existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN). Conclusion: This study introduced a Hadoop Map-reduce model to create diverse mappers and diminish the data utilizing OBL-GWO strategy.

Understanding and Analysing Resource Utilization, Costing Strategies and Pricing Models in Cloud Computing

This paper is primarily focused on understanding the basics of cloud computing economics – specific pricing models and general cost structure and compares between basic cost structures. It also examines 15 pricing models dependent on various bases and attempts to consolidate the different pricing methods used to guarantee maximum profitability to both the service provider and consumer. To avail the services of cloud computing from the cloud service provider, the organization has to incur individual costs. There are three types of costs: fixed, variable, and semi-variable costs. Each type is computed and analyzed. There are more than 30 pricing models though the pay-as-you-go model, subscription model, and pay-for-resources models are in demand. To consider and discuss the basics of cloud computing, emphasizing its economics and pricing models considering various aspects and costing components, using and estimating plans from various cloud Registering suppliers. The paper also considers identifying gaps, if any, present in the current pricing models that are highly preferred with other pricing models that are designed but not implemented due to practical conditions and fear of higher percentage of risk and error and to compare the basic prices provided by the cloud service providers.

A Conceptual Framework for Secure Cloud Migration

Cloud computing is a budding web-based technology that tends to prevail in our environment, especially in the areas of computing and information technology that require large-scale network computing. Cloud computing is a shared set of services gaining popularity due to its profitability, availability, and high production. Just on the security grounds, cloud computing’ immense potential cannot be overlooked: ongoing research and the investigation of consistent, robust, and integrated cloud computing security models may be the solitary motivational pathway. There are three service and four deployment models in cloud computing. Cloud computing, together with its several advantages, also brings a much more difficult condition concerning privacy, authenticated access, data protection, etc. Because of these problems, the acceptance of cloud computing is becoming difficult in the current era. In this study, several security concerns related to reliability and data privacy, main aspects impacting cloud computing, were tackled, and recommendations on specific areas were also discussed.

Challenges in the Cloud Computing Model of Resource Management Issues Such as Service Level Agreements and Services Models

Cloud computing has recently emerged as a new model for hosting and delivering services over the internet. Cloud computing has many advantages, such as the ability to increase capacity or add capabilities without the need to invest in new infrastructure. It can also fulfil technological requirements in a fast and automated manner. In recent years, cloud computing has changed the IT industry; in fact, it is one of the industry's fastest growing phenomena. However, as more information about people and businesses becomes available in the cloud, concerns about the safety of this environment will increase. In addition, some challenges to the use of this service exist. This paper presents the results of a survey about cloud computing and outlines the main concepts of the technology along with examples of appropriate usage. It also discusses resource management issues such as service level agreements and highlights the challenges faced by users when choosing cloud computing services.

Privacy preserving model-based authentication and data security in cloud computing

PurposeCloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform, resources, and accessing the network. Furthermore, cloud computing is a broader technology of communication convergence. In cloud computing architecture, data security and authentication are the main significant concerns.Design/methodology/approachThe purpose of this study is to design and develop authentication and data security model in cloud computing. This method includes six various units, such as cloud server, data owner, cloud user, inspection authority, attribute authority, and central certified authority. The developed privacy preservation method includes several stages, namely setup phase, key generation phase, authentication phase and data sharing phase. Initially, the setup phase is performed through the owner, where the input is security attributes, whereas the system master key and the public parameter are produced in the key generation stage. After that, the authentication process is performed to identify the security controls of the information system. Finally, the data is decrypted in the data sharing phase for sharing data and for achieving data privacy for confidential data. Additionally, dynamic splicing is utilized, and the security functions, such as hashing, Elliptic Curve Cryptography (ECC), Data Encryption Standard-3 (3DES), interpolation, polynomial kernel, and XOR are employed for providing security to sensitive data.FindingsThe effectiveness of the developed privacy preservation method is estimated based on other approaches and displayed efficient outcomes with better privacy factor and detection rate of 0.83 and 0.65, and time is highly reduced by 2815ms using the Cleveland dataset.Originality/valueThis paper presents the privacy preservation technique for initiating authenticated encrypted access in clouds, which is designed for mutual authentication of requester and data owner in the system.

A State of the Art Survey of Machine Learning Algorithms for IoT Security

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

Secure content based image retrieval system using deep learning with multi share creation scheme in cloud environment

In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article introduces an effective secure IR scheme using Inception with ResNet v2 (SIRS-IR) and Multiple Share Creation (MSC) scheme. The proposed method uses Inception with ResNet v2 model based feature extraction process. At the same time, the MSC process is utilized for multiple share generation and then encryption of shares takes place using double chaotic logistic map (DLCM) technique. Besides, the encrypted shares and the respective feature vectors are saved in the cloud server with the corresponding image identification number. During the IR process for the applied QI, the SIRS-IR model extracts the feature vectors and performs similarity measurement to retrieve the related images from the database interms of encrypted shares. Finally, the share decryption process is carried out for the reconstruction of original images with no loss of quality. Extensive experimentations were performed to verify the retrieval performance and image quality of the reconstructed images using Corel10K dataset. The obtained results stated that the presented SIRS-IR model is found to be superior to other methods in a considerable way.

LacminCC: lightweight anonymous communication model in cloud computing

With increasing application of cloud computing and big data technologies, a large amount of personal information is stored on the Internet, which raises the issue of privacy leakage. To protect people’s data privacy, this paper firstly presents a new anonymous Identify-Based Encryption (IBE) scheme and gives the proof of its security under the Bilinear Diffie–Hellman Security Assumption. Then, by introducing the anonymous IBE scheme into anonymous communication fields, this paper introduces a new lightweight anonymous communication model for cloud computing, which guarantees the anonymity of system users and the security of messages in small groups. Our analysis shows that, the proposed communication model cannot only reduce memory consumption and improve message transmission efficiency, but also effectively resist traffic-analysis attacks, node eavesdropping, and finally achieve secure anonymous communication in cloud computing.

E-Commerce Model Oriented to Cloud Computing and Internet of Things Technology

In recent years, the number of e-commerce netizen users in China has grown dramatically, and e-commerce users who shop online have also grown rapidly. In this competitive environment, the rapid development of e-commerce has also risen rapidly. Therefore, it is very necessary to actively carry out research on e-commerce models for cloud computing and internet of things technology. The purpose of this article is mainly to explore the research on e-commerce model for cloud computing and internet of things technology and establish a model for cloud computing and internet of things. It has data mining and distribution functions to test multiple indicators such as cloud computing facilities and enterprise performance. The research results show that the number of complaints on the e-commerce model based on cloud computing and internet of things technology is 2,368; the number of complaints on the B2B e-commerce model is 19,955; the number of complaints on the B2C e-commerce model is 5,016; the number of complaints in C2C e-commerce was 51,854.

A STEAM Learning with Digital Fabrication Laboratory on Cloud Computing Model to Enhance Creative Product

This research was conducted to study and develop a model of a STEAM learning with digital fabrication laboratory on cloud computing model to enhance creative product. The aims of this study were 1) to create a STEAM learning with digital fabrication laboratory on cloud computing model to enhance creative product, and 2) to evaluate a STEAM learning with digital fabrication laboratory on cloud computing model to enhance creative product. The research method was divided into two phases. The first phase involved the design of a model: 1) to study, analyze, and synthesize the con-tents about steam, digital fabrication laboratory, cloud and creative product. 2) to develop a process model of a STEAM learning with digital fabrication laboratory on cloud computing model to enhance creative product, 3) to pre-sent and show the constructed model to advisor and experts in order to get it approved to be able to hold in-depth interviews, and 4) to create the tools for evaluating the appropriateness of the model. The second phase is referred to in the model evaluation. The sample group in this re-search consisted of five experts in the fields of Steam, Information Technology and Instructional Design. Then, the data were analyzed using arithmetic means and standard deviations. The process of developing a model has 3 components that include 9 procedures. This model increases product creativity skills of learners. The experts agreed with the overall model that was shown at a good level.

An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

Analysis of Internal Threat Detection Methods of Power Grid under the Characteristic Cloud Computing Model

Advances in science and technology are driving the development of society, and with the development of society, our power grid coverage has become more extensive, which has solved the power supply problem in most parts of the country. As an important infrastructure of the country, the safety and nursing work of the power grid industrial control system is very important. However, the increase of coverage makes the maintenance and management of the power grid itself more difficult, and the internal threat of the power grid can not be ignored. To get a clearer understanding of the methods and patterns we use in terms of threats within the grid, this article will analyze the objects we are studying us using cloud computing simulation methods, and then analyze the results of the data. The experimental results show that the detection method of internal threat of power grid is still in the preliminary stage of development in our country.

A Blockchain-Based Trusted Edge Platform in Edge Computing Environment.

Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios.

Cloud Computing Models, Issues and Challenges

Cloud Computing Models, Issues and Challenges