Cloud Computing Research Articles

Analysis of Key Technologies for Data Security Protection Based on Cloud Computing

With the rapid development of cloud computing, more and more enterprises and institutions are migrating their data and business operations to the cloud. However, this shift has brought data security issues in cloud environments to the forefront. This paper primarily investigates the key technologies for data security protection in cloud computing environments. It begins by introducing the basic concepts and architecture of cloud computing and analyzing the fundamental concepts and threats related to data security protection. The paper then delves into critical technologies such as data encryption, access control, data storage and backup security, and data privacy protection, detailing the algorithmic principles and implementation methods of these technologies. Additionally, the paper explores the challenges faced by current cloud computing data security protection technologies and their corresponding solutions, analyzing the effectiveness of these technologies through practical case studies. Finally, the paper forecasts the future development trends of cloud computing data security technologies, particularly the potential impact of emerging technologies on data security protection. This research aims to provide theoretical and technical support for data security protection in cloud computing environments.

The Future of Database Management in the Era of Big Data and Cloud Computing

The landscape of database management is undergoing rapid transformation due to the rise of Big Data and cloud computing. Traditional database management systems (DBMS) are evolving to address new demands for scalability, flexibility, and efficiency. This paper explores the future of database management in the context of these advancements, focusing on emerging trends, technological innovations, and the challenges associated with recent developments is examined, providing a comprehensive overview of how database management is adapting and what future directions may hold. The research methods adopts a mixed-methods approach to explore the future of database management in the context of big data and cloud computing. This includes both qualitative and quantitative analyses to provide a comprehensive understanding of current trends, challenges, and advancements.

Efficient load distribution in heterogeneous vehicular networks using hierarchical controllers

Vehicle movement poses significant challenges in vehicular networks, often resulting in uneven traffic distribution. Fog computing (FC) addresses this by operating at the network edge, handling specific tasks locally instead of relying solely on cloud computing (CC) facilities. There are instances where FC may need additional resources and must delegate tasks to CC, leading to increased delay and response time. This work conducts a thorough examination of previous load balancing (LB) strategies, with a specific focus on software-defined networking (SDN) and machine learning (ML) based LB within the internet of vehicles (IoV). The insights derived from this research expedite the development of SDN controller-based LB solutions in the IoV network. The authors proposes the integration of a local SDN controller (LSDNC) within the FC tier to enable localized LB, addressing delay concerns. However, the information will be available to the main SDN controller (MSDNC) too. The authors explore the concept mathematically and simulates the formulated model and subjecting it to a comprehensive performance analysis. The simulation results demonstrate a significant reduction in delay, with a 125 ms difference when 200 onboard units (OBUs) are used, compared to conventional software-defined vehicular networks (SDVN). This improvement continues to increase as the number of OBUs grows. Our model achieves the same maximum throughput as the previous model but delivers faster response times, as decisions are made locally without the need to wait for the main controller.

V2X Communication Computing Resource Collaboration Technology Based on Deep Reinforcement Learning

In traditional cloud computing, data needs to be transferred from distributed data sources to remote cloud data centers for high-performance computing. However, tasks often experience network transmission delays of hundreds of milliseconds or more from data sources to cloud data centers, which does not meet the need for low latency in V2X (Vehicle to Everything) systems. On the basis of the existing heterogeneous task scenario, this paper proposes a communication computing resource collaboration technology scheme based on heterogeneous task, which can meet the requirements of delay and cost when facing tasks with different delay requirements and different divisibility. This scheme uses the communication computing resources of the roadside unit and the service vehicle, so that the computing task of the task vehicle can be completed within the delay requirement and the cost is minimal. We use the deep reinforcement learning method to study, and finally the performance of the proposed scheme is verified by simulation.

Technologies for customized housing in modular construction

In the modular construction, the range of customization options is limited compared to traditional construction, which can be seen as a negative aspect. Different customization strategies can be adopted by companies, but the choice of the appropriate strategy is related to the technologies used in the operational process, with emphasis on the design and production stages. This study aims to compare the technologies used in the design, production and exchange of information in the development of customized housing in modular construction companies in Brazil and England. To achieve that, three case studies were conducted in English modular construction companies operating in the residential sector. The results of the case studies were compared with the literature on technologies used by Brazilian companies. It was noted that English companies, regardless of their size and type of customization offered, use Revit software to develop the design, and communication between different designers is done via cloud computing. However, communication between designers and production is often done on paper, which leads to errors. The machinery used on the shop floor varies between companies. The results found are similar to the Brazilian reality. It was noted that companies do not make use of all available potential; however, larger companies that are able to perform mass customization invest more in technologies for control and integration between operations. To ensure greater productivity and efficient use of technologies, companies should adopt mass customization.

The Impact of Technology in Warehousing

The warehouse business has undergone a revolution with the integration of advanced technology, resulting in the transformation of traditional storage facilities into highly efficient and dynamic settings. The article delves into the ways in which warehousing operations have been transformed by a number of technological advancements, including automation, robotics, WMS, the IoT, AI, cloud computing, and AR. With increased efficiency, precision, and cost-effectiveness, these technologies have made it possible to manage inventories more effectively, fulfill orders more quickly, and make better decisions. These technology developments are expected to have an even greater impact on how supply chain management and logistics are developed in the future as the warehousing sector develops.

Development and evaluation of a training curriculum to engage researchers on accessing and analyzing the All of Us data.

The All of Us Evenings with Genetics (EwG) Research Program at Baylor College of Medicine (BCM), funded to engage research scholars to work with the All of Us data, developed a training curriculum for the Researcher Workbench, the platform to access and analyze All of Us data. All of Us EwG developed the curriculum so that it could teach scholars regardless of their skills and background in programming languages and cloud computing. All of Us EwG delivered this curriculum at the first annual All of Us EwG Faculty Summit in May 2022. The curriculum was evaluated both during and after the Faculty Summit so that it could be improved for future training. Surveys were administered to assess scholars' familiarity with the programming languages and computational tools required to use the Researcher Workbench. The curriculum was developed using backward design and was informed by the survey results, a review of available resources for training users on the Researcher Workbench, and All of Us EwG members' collective experience training students. The curriculum was evaluated using feedback surveys during the Faculty Summit as well as virtual meetings and emails following the Faculty Summit. The evaluation results demonstrated the success of the curriculum and identified areas for improvement. The curriculum has been adapted and improved in response to evaluations and in response to changes to the All of Us data and infrastructure to train more researchers through this program and other scholarly programs.

Detailed Cloud Linear Regression Services in Cloud Computing Environment

Detailed Cloud Linear Regression Services in Cloud Computing Environment

Experimental performance of smart irrigation systems using IoT

In agriculture field soil is a crucial determinant of plant development, primarily in irrigated environments. Nowadays there are several ways for assessing soil quality on a density or suspense basis. To develop, all plants need a specific amount of moisture level. PI involves cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and cloud computing. In this method presents an overview of the soft computing methods based on the PI concept and architecture, including the most common wireless technologies. Then, a real-time IoT-based intelligent irrigation system is designed as a proof of concept. Several wireless sensor nodes are deployed to monitor both soil moisture and temperature. Sensed data are transmitted to the gateway through the Messaging Queuing Telemetry Transport (MQTT) communication protocol. Root based findings depict that the proposed work increases crop growth and productivity and decreases water wastage more than other state-of-the-art strategies. ANFIS method to predict the plant growth level based on the fuzzy rule. Wireless communication technology allows for the automation of different devices and real-time analysis of data collected by these sensors. The IoT (Internet of Things) system demonstrates improved plant growth. Furthermore, the application used in the plant's worst conditions and situations has provided the best sources for appropriate plant growth..

Transformasi Digital: Efisiensi dan Inovasi dalam Manajemen Operasional

Operational management is one of the important fields in management science that focuses on managing resources effectively and efficiently to achieve organizational goals. With the increasing development of information and communication technology, it has given birth to digitalization in various sectors. However, to what extent this digital transformation is able to increase efficiency and realize innovation in operational management, an in-depth analysis is needed. The method used in this study is descriptive qualitative, using secondary data analysis. Data collection and literature studies were carried out to determine the right analysis so that it can produce conclusions that are useful for increasing competitiveness and ensuring business continuity. In this study, the adoption of technologies such as AI, IoT, and automation is the key to achieving increased efficiency and opening up opportunities for innovation that have a significant impact on operational management and company performance. Digitalization is the foundation for companies to be competitive and relevant in an increasingly competitive global market. In other words, digital transformation is absolutely necessary, because it is proven that the adoption of digital technologies such as AI, big data, cloud computing, and IoT not only increases the efficiency of operational management, but also paves the way for continuous innovation and rapid adaptation to market changes.

Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions

Cloud computing is a method of providing various computing services, including software, hardware, databases, data storage, and infrastructure, to the public through the Internet. The rapid expansion of cloud computing services has raised significant concerns over their environmental impact. Cloud computing services should be designed in a green manner, efficient in energy consumption, virtualized, consolidated, and eco-friendly. Green Cloud Computing (GCC) is a significant field of study that focuses on minimizing the environmental impact and energy usage of cloud infrastructures. This survey provides a comprehensive overview of the current state of GCC, focusing on the challenges, strategies, and future directions. The review study begins by identifying important challenges in GCC from practical implementations, identifying GCC-introduced environmental protection and prevention initiatives, and expressing the demand for long-term technical progression. It then addresses GCC’s primary concerns, such as energy efficiency, resource management, operational costs, and carbon emissions, and categorizes implementations according to algorithms, architectures, frameworks, general issues, and models and methodologies. Furthermore, enhancements in virtualization, multi-tenancy, and consolidation have been identified, analyzed, and accurately portrayed to address the advancements in GCC. Finally, the survey outlines future research directions and opportunities for advancing the field of GCC, including the development of novel algorithms, technologies for energy harvesting, and energy-efficient and eco-friendly solutions. By providing a comprehensive overview of GCC, this survey aims to serve as documentation for further evolving new emerging technological approaches in the GCC environment.

The Role of Cloud Computing in Modern IT Infrastructure: Opportunities and Risks

With cloud computing, businesses and organizations may now use computer resources on-demand over the internet, making it a crucial invention in the field of information technology. It has redefined IT infrastructure by offering scalable, flexible, and cost-effective solutions that transform the way data and applications are managed. However, while the benefits are numerous, cloud computing presents challenges, particularly concerning security, privacy, and legal issues. This paper aims to explore the role of cloud computing in modern IT infrastructure, highlighting its opportunities and risks. The paper will cover cloud computing’s architecture, service models, and deployment types, followed by a detailed discussion on its advantages and potential drawbacks, including security, privacy, and compatibility concerns.

Fostering inclusive welfare and Islamic financing through Islamic social finance digitalization strategy

Islamic Social Finance (ISF) is one solution for poverty alleviation, which is a major concern in developing countries like Indonesia. However, the performance of ISF in Indonesia, both in terms of collection and management, is still far from expectations. To address this problem, a digital transformation of ISF is needed by leveraging technologies that come with both advantages and disadvantages. This study employs a Delphi ANP-BOCR analysis to determine the most optimal technology for ISF digitalization, which result is supported by meta-analysis findings. The result shows that cloud computing is the most optimal technology when only benefits and costs are evaluated. When all factors, including benefits, opportunities, costs, and risks, are considered, digital platform proves to be the most optimal technology for both the short and long term. BOCR analysis also confirms that digital platform and cloud computing are the most optimal technology. Meta-analysis further supports the contribution of digital platform in improving social finance performance. To optimize the implementation of ISF digitalization in Indonesia, this study provides an alternative model that incorporates multiple technologies.

A preliminary discussion on data privacy and data security issues

With the expeditious evolution of technologies such as the internet, big data, cloud computing, and artificial intelligence, data has ascended to the status of an invaluable and indispensable resource within the intricate tapestry of modern society. Yet, the labyrinthine ethical quandaries that intertwine with the processes of data collection, storage, processing, and application have progressively surfaced, capturing the rapt attention of academia, industry, and government as they grapple with this nascent challenge. However, with the extensive application of data technology, issues of data ethics have become increasingly prominent, posing unprecedented challenges to personal privacy, data security, social equity, and justice. This article aims to delve deeply into the connotation and extension of data privacy and data security, as well as its significant role in current society. By examining relevant research findings at this stage, analyzing the major challenges faced by data ethics and exploring corresponding strategies, this paper delves into the multifaceted challenges faced by data privacy and data security, highlights the inadvertent data collection by smart devices, the potential for re-identification of blurred and anonymized information through big data analysis, as well as the ongoing threats posed by hacker attacks, internal vulnerabilities, and the complexity of data circulation.

Explore the Evolution of Computer Network Architecture

Since the invention of computer networks in the 1960s, their architecture has undergone many major innovations and evolutions. This review aims to sort out and analyze this evolution process and reveal its internal motivations and fundamental driving forces. We will analyze the development of network architecture from multiple dimensions such as network topology, protocol stack, and control mechanism, and reveal the internal logic of the architecture evolving with the times.First, the paper reviews the origins of computer network development, explains the original packet switching ideas and ARPANET architecture, and analyzes its influence on later generations. Then it focuses on the birth of the TCP/IP protocol family and its architecture, analyzing its design concepts and innovations. Subsequently, the paper will explore the rise of the Internet and the formation of its layered architecture model, commenting on the advantages and disadvantages of this model. We will also examine the ideas and attempts of the next generation Internet architecture, such as active networks and content-centric networks, and analyze their challenges and limitations.In addition, the paper will explore the new changes brought about by emerging network paradigms such as software-defined networking (SDN) and network function virtualization (NFV) to network architecture, and comment on their advantages and impacts. We will also focus on the driving role of emerging paradigms such as cloud computing and edge computing on network architecture. Finally, the paper will look forward to new trends and challenges in the development of future network architecture, such as the potential application prospects of cutting-edge technologies such as artificial intelligence, machine learning, and blockchain in network architecture. By comprehensively analyzing and summarizing the evolution of network architecture, the paper hopes to provide readers with a macro perspective and a deep understanding of the internal driving force and development logic of network architecture innovation. This will not only help us understand the past, but also provide inspiration and ideas for future network architecture innovation.

THE FUTURE OF MOBILE CLOUD COMPUTING: KEY IMPLICATIONS AND CHALLENGES

This research aims to examine the future development of Mobile Cloud Computing (MCC) and identify the key implications and challenges that this technology will face. In the face of the technological revolution, especially with the adoption of 5G networks and integration with the Internet of Things (IoT), MCC has the potential to transform various industry sectors. The study employs a qualitative methodology through a literature review and semi-structured interviews with experts in cloud technology, mobile computing, and technology policy. The literature review is conducted to understand the latest technological trends and the challenges faced by MCC, while interviews provide direct insights from industry practitioners and policymakers about the future of MCC. The research findings indicate that MCC will increasingly integrate with 5G technology and IoT, enabling the adoption of more complex applications such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI). However, several key challenges need to be addressed, such as data security issues, reliance on network infrastructure, and regulations that are not yet fully adaptive to MCC developments. Experts emphasize the importance of enhancing security standards, developing edge computing to reduce latency, and fostering collaboration between governments and industry to create policies that support innovation.

Transforming Clinical Research: The Power of High-Throughput Omics Integration.

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Enhancing rural healthcare through internet-based remote collaborative outpatient services: A comprehensive evaluation in Changzhi, Shanxi Province.

The advancement of digital technology, particularly telemedicine, has become crucial in improving healthcare access in rural areas. By integrating cloud computing and mHealth technologies, Internet-based Collaborative Outpatient Clinics offer a promising solution to overcome the limitations of traditional healthcare delivery in underserved communities. A trial was conducted in 4 counties of Changzhi City in Shanxi Province, China. The system extended to 495 rural communities and served over 5000 rural residents. Deep learning algorithms were employed to analyze medical data patterns to increase the accuracy of diagnoses and the quality of personalized treatment recommendations. After the implementation of the system, there was a significant improvement in the satisfaction levels of rural residents regarding medical services; the accuracy of medical consultations increased by 30%, and the convenience of medical access improved by 50%. There was also a notable enhancement in overall health management. Satisfaction rates among healthcare professionals and rural inhabitants were over 90% and 85%, respectively, indicating that the system has had a significant positive impact on the quality of health-care services. The study confirms the feasibility of implementing telemedicine services in rural areas and offers evidence and an operational framework for promoting innovative healthcare models on a large scale.

A systematic literature review for load balancing and task scheduling techniques in cloud computing

Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques.