Cloud Computing Research Articles

Agile tax technology development in the U.S.: A conceptual framework for scalable and efficient enterprise solutions

The rapid evolution of tax regulations in the United States, coupled with increasing digital transformation in financial compliance, necessitates the development of agile, scalable, and efficient tax technology solutions. Traditional tax management systems, often rigid and monolithic, struggle to adapt to dynamic regulatory changes, increasing enterprise demand for flexible, technology-driven approaches. This review explores Agile Tax Technology Development as a conceptual framework that integrates agile methodologies with advanced digital solutions to enhance efficiency, scalability, and compliance in enterprise tax management. Agile methodologies, such as Scrum, Kanban, and SAFe, provide iterative and adaptive software development models that allow tax technology teams to rapidly respond to regulatory updates and evolving business needs. The review examines key components of agile tax technology, including cloud-based infrastructure, API-driven architectures, automation, and artificial intelligence, which collectively enable real-time tax processing, predictive analytics, and seamless integration with financial systems. Additionally, it discusses the role of cross-functional collaboration among tax professionals, software developers, and regulatory experts to ensure compliance and continuous improvement. Scalability is a critical factor in enterprise tax solutions, requiring cloud computing, microservices architecture, and distributed ledger technologies to efficiently process vast amounts of tax data. This study highlights best practices in Agile Tax Technology Development by analyzing case studies from leading enterprises and government initiatives. Furthermore, it addresses challenges such as integrating agile frameworks into legacy tax systems, ensuring cybersecurity in financial data processing, and overcoming organizational resistance to agile adoption. Ultimately, this review provides a forward-looking perspective on the future of tax technology in the U.S., emphasizing the need for continuous innovation, automation, and agility in response to an increasingly complex regulatory landscape. The findings serve as a guideline for enterprises seeking to modernize their tax compliance strategies while maintaining operational efficiency and regulatory adherence. Keywords: Agile Tax Technology, U.S, Efficient Enterprise Conceptual Framework.

Efficient Resource Allocation to Enhance the Quality of Service in Cloud Computing

Pay-as-you-go models are used to grant users access to cloud services. While using the cloud, an imbalance workload on data centre resources degrades quality of service metrics like makespan, storage, high failure rate, and energy consumption. Hence proposed the heuristic based hybrid GA to enhance the QoS with resource allocation in cloud computing. The population is first initialized using the Binary encoding sorts the tasks according to priority. After that, the Best Fit algorithm compares the Best Fit with iterations of each fitness value depending on the computation time to shorten the make span. Heuristic crossover approach and mutation are then used to update the probability of the existing population with the new population lowers the failure rate by using the fitness value. Therefore, the proposed heuristic-based hybrid GA technique balanced the load and allocate the resources effectively to improve QoS performances. The outcome reveals that the proposed method of QoS performances attained less makespan, energy consumption, failure rate and execution time with effectively allocated the resources of 1% to 39% when compared to the previous methods in cloud computing.

Impact of national institutions on cloud computing adoption. Comparison to mobile broadband adoption

ABSTRACT Research shows that the adoption of cloud computing varies across countries, giving a rise to the obvious question as to why is that the case. This paper tests the proposition that national institutions, both formal and informal (such as national culture), affect cloud computing adoption in a country. To develop hypotheses, a specific perspective from neo-institutionalism theory has been drawn, focusing on the impact of national institutions on technology adoption. In an empirical examination of 38 countries’ cloud computing revenues over a five-year period, it was found that four out of six selected national institutions significantly impact cloud computing adoption in a country. Those institutions being power distance, uncertainty avoidance, trade union strength and government effectiveness. Those results are further contrasted and compared to mobile broadband adoption as an example of an also recent; however, it is consumer-oriented technology, while cloud computing is predominantly business-oriented technology. The comparison is done to test if cloud computing has different institutional drivers when compared to a very different technology such as mobile broadband. In an empirical examination of 41 countries’ mobile broadband revenues over a three-year period, it was found that none of the six selected institutions significantly impacts mobile broadband adoption. Thus, the adoption processes of cloud computing and mobile broadband, two very different technologies, possess rather different institutional drivers.

Developing a framework for cloud computing adoption in academic libraries: an empirical investigation from university librarians

Purpose This study aims to investigate the adoption of cloud computing by university libraries in Punjab province, Pakistan, identify the awareness of university librarians about the use of cloud computing in libraries, reveal the problems faced by university libraries in adopting cloud computing and develop a framework for cloud computing adoption in academic libraries. Design/methodology/approach This study used a quantitative research method by using a questionnaire as an instrument to address the study’s objectives. A total of 65 university libraries of Punjab Province were the population of the study. A purposive sampling technique was used. The SPSS software was used for the analysis of data. Findings The study found that the majority of university libraries in the Punjab province have the necessary information and communication technology (ICT) infrastructure. Among the three cloud computing models (SaaS, PaaS and IaaS), Software as a Service (SaaS) was the most widely adopted model in university libraries. The study identified various cloud-based services provided by university libraries. Librarians showed limited awareness of cloud service providers, with the majority only being aware of Amazon Web Services. Librarians acknowledged various challenges in adopting cloud-based library services, including poor internet connectivity, high subscription and renewal costs, data security and privacy concerns. Originality/value This study has provided a framework based on empirical findings of the study to efficiently adopt cloud-based library services in academic libraries.

Decentralized dynamic load balancing for virtual machines in cloud computing: a blockchain-enabled system with state channel optimization

Decentralized dynamic load balancing for virtual machines in cloud computing: a blockchain-enabled system with state channel optimization

Attack Detection Using an Ensemble Model with Improved Score Level Fusion in Cloud Computing

Attack Detection Using an Ensemble Model with Improved Score Level Fusion in Cloud Computing

Digital transformation of tourism education: Technologies, opportunities, challenges, and future research agenda

This study explores the integration of these technologies into tourism curricula, highlighting both opportunities and challenges. While digital tools enhance experiential learning, accessibility, and industry relevance, their adoption is hindered by high costs, faculty training, and resistance to change. The study employs qualitative research through semi-structured interviews with tourism educators to examine the effectiveness of digital tools and their impact on student learning. Findings indicate immersive technologies improve engagement, while cloud computing and AI facilitate data-driven decision-making. However, further research is needed to address institutional barriers and ensure equitable access to digital education. The study concludes with a future research agenda to optimize digital transformation strategies in tourism education.

Why Cybersecurity Is a Corporate Priority in the Automotive Industry

The automotive industry is going through a rapid transformation due to technological advancements like connected cars (V2X technologies), artificial intelligence, autonomous driving, 5G, and cloud computing. These changes provide opportunities and numerous challenges to OEMs, suppliers, and other stakeholders. This review paper first lists the various threats encountered by the industry on different vehicle attack surfaces. It highlights the adoption of different technologies in modern cars and the potential threats they may bring. It finally justifies why cybersecurity has become a corporate priority for stakeholders in the automotive industry. Keywords: Connected Vehicles, Cybersecurity, Connectivity, CAN (Controller Area Network), Autonomous, V2X(Vehicle-to-Everything), Artificial Intelligence.

Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey

The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.

An IoT-Enabled Cloud Computing Model for Authentication and Data Confidentiality using Lightweight Cryptography

An IoT-Enabled Cloud Computing Model for Authentication and Data Confidentiality using Lightweight Cryptography

Cloud Computing Adoption in Government Organizations in Developing Countries

Cloud Computing Adoption in Government Organizations in Developing Countries

How Does Cloud Computing Adoption Accelerate Education? – Enhancing Academic Results by Integrating Knowledge Management Pre and Post Pandemic

This research seeks to identify critical factors that impact adoption of cloud services in higher education institutes (HEIs) and provide a comparative analysis between pre and post COVID scenarios. To formulate a more comprehensive adoption model for cloud services, authors have amalgamated the Technology Acceptance Model with categorical variables and conducted a comparative analysis for Tier 1 and Tier 2 cities in India for both periods (i.e., pre and during COVID). Data was obtained from 867 students studying in 25 different Indian universities. The results show that knowledge sharing, security, and privacy are associated with perceived usefulness. Results indicate that cloud computing adoption does lead to innovation, which in turn, improves the academic performance of HEIs. We believe that the findings of our study provide suggestive evidence acknowledging the benefits of cloud computing adoption in enhancing academic performance in HEIs, especially in the context of a developing country.

Transforming Healthcare with Real-Time Big Data Analytics: Opportunities, Challenges, and Future Directions

Real-time big data analysis is already disrupting healthcare by changing the way decisions are made, treatment given, and organizations and processes managed. Using technologies like AI, IoT, and cloud computing, healthcare organizations can analyze large data sets alerting patients to diseases, picking appropriate treatments, and determining where resources will be essential. In this article, we explores the progress, opportunities, and issues of real-time analytics in the healthcare industry and the sort such as predictive analytics, patient supervision, and telemedicine implementation. Despite the ethical issues, data privacy and system integration issues remain potent barriers, trends such as precision medicine and digital health mapping of the world present the implementation of a brave new world in the delivery of healthcare. For this, there is a need for innovation through embracing technology, sector collaboration and, most importantly, proper regulation in order to foster and promote secure usage for people.

Predictive Stress Management for Employees Using Cloud Computing and Machine Learning

Currently, workplace stress is a prevalent occurrence among employees in organisations. Management must assess the mental state and physical capabilities of personnel prior to task assignment. The constantly evolving demands of the operational environment might elevate stress levels, particularly for individuals who continually operate under pressure. While pressure can enhance performance, excessive pressure may induce stress, adversely affecting the workforce. Workplace stress is an escalating issue within businesses, as it adversely impacts both employees' professional and familial lives. Work stress denotes the pressure or tension individuals experience in their lives. This is seen as a significant factor affecting organisational welfare and personnel health. It influences employee behaviour in organisations. It significantly influences employee motivation and satisfaction. Employee productivity and the overall productivity of the company are influenced by stress and motivation levels. Occupational stress induces a range of psychological issues, including anger, despair, anxiety, impatience, and tension. These factors significantly influence employee motivation.

Optimizing English translation processing of conceptual metaphors in big data technology texts

Focusing on the difficulties presented by conceptual metaphors in Big Data (BD)-related literature, this research offers a novel methodology for improving English translation processing. The main goal of this study is to improve translation efficiency and accuracy via cutting-edge technologies, including machine learning, cloud computing, and big data analytics. English texts with complex metaphors are gathered and annotated for the study, and then the suggested model is compared to more conventional translation techniques. The results show that the optimized translation model performs better than traditional methods. Evaluation measures, particularly the Translation Edit Rate (TER) and Bilingual Evaluation Understudy (BLEU), show that the model records lower TER scores, which indicate fewer changes required for correctness, and higher BLEU scores, which indicate enhanced translation quality. The optimized model’s performance stabilizes as the amount of text rises, demonstrating how resilient it is to processing bigger datasets. This study shows how well the suggested model enhances translation results and illustrates how crucial it is to comprehend metaphorical language in technical situations. This research aims to give translators a better foundation for handling the difficulties of writing about Big Data by tackling the nuances of interpreting conceptual metaphors. Finally, given the quickly changing world of technology, the knowledge gathered from this study advances translation techniques and improves interlingual communication.

Does Culture Affect Post-Adoption Privacy Concerns of Mobile Cloud Computing App Users? Insights from the US, the UK, and India

Does Culture Affect Post-Adoption Privacy Concerns of Mobile Cloud Computing App Users? Insights from the US, the UK, and India

TrusO- A Secured Approach Data Transmission from IOT to Cloud

IoT is the intercommunication between electronic devices that acts as an intelligent sensing system. The IoT is connected to the internet to share data among various electronic devices to work intelligently and efficiently. Massive amounts of data are shared among the devices transferred through the insecure internet for storage. Cloud computing is a tremendous emerging technology to store large amounts of data related to the IoT. Data privacy is a significant issue in cloud computing. The data, which should be highly confidential, are transferred by the IoT devices, and will be stored in the cloud. The data in the cloud are highly vulnerable to various attacks namely Man-in-The-Middle attack, Impersonation attack, spoofing attack, Password Guessing attack etc. To avoid particular situations, this article proposes an access control Attribute-Based Encryption (ABE) method to store the data in the cloud using a Trusted Organizer (TrusO) in NTRU encryption. With this approach, the data are proved to be confidential during the data transmission and while stored in the cloud infrastructure. The data stored in the cloud shall be accessed by trusted parties, and TrusO will rectify unwanted data leakage. So it ensures data privacy, confidentiality, and access control to the data in the cloud that are transferred by the IoT devices.

CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments

ABSTRACTBackgroundCloud Computing has established itself as an efficient and cost‐effective paradigm for the execution of web‐based applications, and scientific workloads, that need elasticity and on‐demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large‐scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large‐scale scenarios.AimThis paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re‐engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment.MethodsAs part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase.ResultsThe result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run‐time performance and memory efficiency (up to 25\% less heap memory allocated), as well as increased flexibility, ease‐of‐use, and extensibility of the framework.ConclusionThese improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next‐generation Cloud Computing environments.

Advancing Security and Efficiency in IoT Healthcare Applications with Challenges Benefits and Latency Reduction Techniques

With the expansion of the Internet of Things (IoT) and cloud computing, conventional reliance on centralized cloud data centres for the storage, analysis, and real-time processing of vast data volumes presents significant challenges. This is particularly applicable to the elevated latency and stringent security demands necessary for real-time IoT healthcare applications. Critical and time-sensitive applications, including e-healthcare, telemedicine, and robotic surgery, necessitate ultra-low latency and stringent security measures. Suboptimal processing, connectivity, and networks impede the performance of these applications. Moreover, conventional cloud designs frequently fail to provide the necessary Quality of Service (QoS) for IoT healthcare systems. Consequently, this essay also explores latency reduction and security improvement techniques in IoT healthcare focusing on the need for information transmission in the respective areas. It wishes to list the first principles for approaches that reduce latency and protect communications, and computational architectures that can operate with such systems. It also explores the features that are crucial for understanding latency and security, as well as comparing several methods of addressing latency reduction and improvement in security alongside their effectiveness. It critically assesses previous approaches, identifies gaps in the literature and emphasizes unanswered questions in the study that may be useful in other works in this field. This research incorporates these findings into propelling concepts relative to IoT device connection and enhancing general paradigms relating to the better employment of healthcare applications. These findings support the need to design future IoT healthcare systems that will be latent sensitive, security-enhanced, and high-performance systems due to emerging characteristics of IoT in healthcare.

Role of artificial intelligence in smart grid – a mini review

A smart grid is a structure that regulates, operates, and utilizes energy sources that are incorporated into the smart grid using smart communications techniques and computerized techniques. The running and maintenance of Smart Grids now depend on artificial intelligence methods quite extensively. Artificial intelligence is enabling more dependable, efficient, and sustainable energy systems from improving load forecasting accuracy to optimizing power distribution and guaranteeing issue identification. An intelligent smart grid will be created by substituting artificial intelligence for manual tasks and achieving high efficiency, dependability, and affordability across the energy supply chain from production to consumption. Collection of a large diversity of data is vital to make effective decisions. Artificial intelligence application operates by processing abundant data samples, advanced computing, and strong communication collaboration. The development of appropriate infrastructure resources, including big data, cloud computing, and other collaboration platforms, must be enhanced for this type of operation. In this paper, an attempt has been made to summarize the artificial intelligence techniques used in various aspects of smart grid system.