Cloud Computing Service Model Research Articles

Analyzing Service Models in Cloud Computing and Networking through Big Data Lens

Cloud computing provides small and medium-sized businesses with the assurance of significant data application. The MapReduce programming standard is used for Big Data processing. However, the application of the MapReduce standard generally requires online connected storage space and similar processing capabilities. The computing requirements of MapReduce programming are typically beyond the capabilities of small and medium-sized businesses. In this paper, we briefly discussed cloud computing service models and network infrastructure driven by big data.

Quantitative Analysis of the Impact of Cloud Computing Service Models on the Employment Structure of College Graduates

Quantitative Analysis of the Impact of Cloud Computing Service Models on the Employment Structure of College Graduates

Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
 Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.

The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.

Retracted: Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions

Retracted: Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions

Cyber resilience and cyber security issues of intelligent cloud computing systems

It is necessary to provide the cyber security of cloud computing according to the components that constitute its structure. The first step in advancing the cyber security of this technology is to accurately identify its threats. In this paper, a new cyber security reference model of the cloud system, which consists of components making up separate layers of cloud computing is proposed. Available reference models of cloud computing security do not describe the virtualization and service layers and the important components for providing the cyber security of cloud computing in detail, do not consider the social media IoT sensor layer, which collects the text data typed by attackers to carry out cyber attacks on the cloud infrastructure, and the cyber resilience issues of the cloud computing at all In addition, this paper studies the cyber security issues of cloud computing service models, and constructs an attack model to provide security of cloud systems. It gives an interpretation of standards and legislative acts on the cyber security of cloud computing. According to security aspects, clarification of the cyber security and cyber resilience concepts of cloud systems is provided. The cyber resilience architecture of intelligent cloud systems is developed. The advantage of developed cyber resilience model over available one is that, it determines the information security and cyber security aspects of cloud computing and combines them to form the cyber resilience aspects of cloud systems.

MAIN TYPES OF CLOUD SERVICES - EXPLAINED

<p style="text-align: justify;"><span lang="EN-US">The advent of cloud computing has revolutionized the IT industry, transforming the way organizations store, manage and process data. This paper provides a comprehensive overview of the beginnings of cloud services, tracing their evolution from early concepts to the sophisticated solutions we have today. In addition, the document highlights the main benefits and challenges associated with cloud services. </span><span lang="EN-US">This paper provides a s comparative study of IaaS, PaaS and SaaS, examining their architectures, functionalities and use cases. It explores the similarities and differences between these models, discusses their strengths and challenges, and analyzes their impact on organizations and end users. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are the three main cloud computing service models, each offering different benefits and features. The document also discusses the major players in each category and offers insight into future trends and potential synergies between the three service models.</span></p>

Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

A Review of Cloud Computing Service Models

Cloud computing provides an infrastructure to process all types of data resources and also used for computing the data resources. It provides a good environment to work with data of large amounts. Despite all the technologies it has become the most important technology of the 21st century. In IT departments it focuses on strategic projects rather than to keep the data in running form. By understanding the solitary attributes and deliberation linked to each model. Any business or company can use it to take selective decision when they will worry about to select most profitable cloud computing for the maximum and specified requirement and ambition or intention. In this research paper we will present a comprehensive review on the different types of cloud computing services model. These models are used to provide intuition for consortium and to make decision- creator to purchase cloud technology. We are also discussing about the benefits, characteristics and comparison among IaaS, PaaS and SaaS.

Cloud-Based Remote Sensing for Wetland Monitoring—A Review

The rapid expansion of remote sensing provides recent and developed advances in monitoring wetlands. Integrating cloud computing with these techniques has been identified as an effective tool, especially for dealing with heterogeneous datasets. In this study, we conducted a systematic literature review (SLR) to determine the current state-of-the-art knowledge for integrating remote sensing and cloud computing in the monitoring of wetlands. The results of this SLR revealed that platform-as-a-service was the only cloud computing service model implemented in practice for wetland monitoring. Remote sensing applications for wetland monitoring included prediction, time series analysis, mapping, classification, and change detection. Only 51% of the reviewed literature, focused on the regional scale, used satellite data. Additionally, the SLR found that current cloud computing and remote sensing technologies are not integrated enough to benefit from their potential in wetland monitoring. Despite these gaps, the analysis revealed that economic benefits could be achieved by implementing cloud computing and remote sensing for wetland monitoring. To address these gaps and pave the way for further research, we propose integrating cloud computing and remote sensing technologies with the Internet of Things (IoT) to monitor wetlands effectively.

Review on security issues and solutions in cloud computing

Cloud computing is the on-demand availability of a group of networked elements providing services, especially data storage and computing power, without actual management and addressing by the user. In lieu, the entire suite of hardware and software is managed by a provider and have functions dispersed over various location, each location being a data center. Since cloud computing users rely on sharing resources and may request multiple cloud services concurrently, a dynamic provision must be there so that all the resources are available to the user in a competent manner. However, despite abundantly available benefits, many organizations are still skeptical about using the cloud computing service model because of security concerns associated with the service. The paper also discusses different types of cloud security, controls of cloud security and algorithms used for emanating the privacy of the client’s data. In this paper, a CNN and SVM model is also applied for detection Anti-Spoofing images for identification on images on cloud.

Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions

Cloud computing is a new model for providing computing services, where computing is delivered as a service over the Internet. Cloud computing is a very popular choice among SMEs because computing services are provided at much lower prices compared to their own IT infrastructure. In the cloud computing model, computing services and data storage are outsourced to cloud service providers. Customers do not have full control over applications and their data. Therefore, an added overhead of security risks comes along, and the security of data becomes the primary concern for cloud customers when considering cloud services. This paper explores various deployment models of cloud computing services and IoT (Internet of Things), figures out associated data security issues with them, and suggests a metric-based solution to assess security provided by cloud services.

A Case Study of Cloud Computing Service Models for the General Computing Environment in a University

Cloud computing technology has emerged as the basic infrastructure technology in the 4th industrial revolution and has been utilized in various fields. Therefore, the authors analyze the requirements of academic and administrative affairs, education affairs, and affiliated institutions in the university. Proposed cloud computing service models in the universities are implemented using an OpenStack in CentOS 7.3 and Windows. To evaluate the performance, the author compares various performance factors including launching time, booting time, ping time, and software installation time. Lastly, the economic benefits in the university will be evaluated by comparing the adoption cost, maintenance cost, and total cost between the existing client-server system and cloud computing system. Future work aims to implement a cloud computing system that can be applied to the entire university. Finally, based on these studies, the authors will implement a regional base cloud system that can connect all universities in the region with one cloud computing system.

Migration of a Research Library's ICT-Based Services to a Cloud Platform

Libraries have been at the forefront in adopting emerging technologies to manage the library’s operations and provide information services to the user community they serve. With the emergence of cloud computing (CC) technology, libraries are exploring and adopting CC service models to make their own services more efficient, reliable, secure, scalable, and cost-effective. In this article, the authors share their experience migrating some of the library’s locally hosted ICT-based services onto the Microsoft Azure cloud platform. The migration of services to a cloud platform has helped the library significantly reduce the downtime of its services due to power or network or system outages.

Exploring of Potential of Cloud Computing for Small and Medium Enterprises

Business activities such as human resources management, payroll, finance, and accounting are crucial for Small and Medium Enterprises (SMEs). Therefore, adopting technologies such as cloud computing is expected to improve SMEs’ efficiency. The migration from current business practices to cloud computing amongst SME entrepreneurs remains a challenge. Therefore, this study presents a short review of cloud computing concepts, the characteristics, types of cloud computing service models, and also cloud computing deployment models. This study highlights the benefits and challenges faced by SMEs entrepreneurs in adopting cloud computing. Also, this study explores the existing cloud computing services provided for SMEs. The present study aims to provide a better understanding of cloud computing’s potential to be applied in helping SMEs manage their business activities.

A Survey of Comparison Different Cloud Database Performance: SQL and NoSQL

The scales in databases have recently increased, and more expansion is expected in the future. The storage costs gradually have slowed down and quickly expanded the storage capacity. Cloud intrusion has modified the comparisons in recent years. Database performance plays an essential part in the market. Cloud Database focuses on new, conventional databases, specific applications for scalability, dynamic devices, and ease of use. Cloud databases are primarily used for data storage, retrieval, modification, and analysis by tools like business intelligence. These tools can build new business strategies and demonstrate scalability and elasticity while managing vast amounts of data with reliable, customized, and cost-effective services in various applications. This paper provides an overview of cloud computing, Cloud database architecture and kinds, and database as a service. It also highlights the characteristics, deployment, and service model of cloud computing and the performance and functionality of the various SQL and NoSQL cloud database applications and services required to evaluate them. It focuses on the different parameters to assess their performance, such as ease of software portability, transaction capabilities, and the maximum amount of data stored. The primary purpose of this paper is to assist businesses and individuals in understanding how cloud computing may provide them with dependable, personalized, and cost-effective services in many applications.

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

With the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

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.

Business process modeling with cloud computing support

Precise and clear Business Process Modeling (BPM) can be used to implement the all of processes according to the user’s needs. On the other hand, cloud computing has become a solution for data storage and more effective and efficient computing processes. State Polytechnic of Malang (Polinema) is one of the vocational higher education Institutions in Indonesia that requires business process modeling to facilitate an integrated understanding of process flow. This study aims to describe the business processes of the financial realization system and the structure of cloud computing that can be used. This research used Bizagi for designing Business Process Model and Notation (BPMN) in modeling business processes of financial system realization. This paper describes the business model of the financial realization system at Polinema. This study proposes the use of an Infrastructure as a Service IaaS-based cloud computing service model using a hybrid cloud approach to support the work of the system.

Compute functional analysis leveraging the IAAS private cloud computing service model in packstack development

Analyzing compute functions by utilizing the IAAS model for private cloud computing services in packstack development is one of the large-scale data storage solutions. Problems that often occur when implementing various applications are the increased need for server resources, the monitoring process, performance efficiency, time constraints in building servers and upgrading hardware. These problems have an impact on long server downtime. The development of private cloud computing technology could become a solution to the problem. This research employed Openstack and Packstack by applying one server controller node and two servers compute nodes. Server administration with IAAS and self-service approaches made scalability testing simpler and time-efficient. The resizing of the virtual server (instance) that has been carried out in a running condition shows that the measurement of the overhead value in private cloud computing is more optimal with a downtime of 16 seconds.