Cloud Adoption Research Articles

The Role of FinOps in Large-Scale Cloud Cost Optimization

The rapid adoption of cloud computing in large enterprises has led to significant challenges in managing cloud costs, particularly as organizations scale their cloud environments across multiple services and providers. FinOps, or Financial Operations, has emerged as a crucial framework for addressing these challenges by fostering collaboration between finance, technology, and operations teams to optimize cloud expenditures. This discipline is designed to enhance financial accountability and visibility, ensuring that cloud resources are used efficiently and in alignment with business objectives. In large-scale cloud environments, where cloud resources are dynamic and often unpredictable, FinOps provides the necessary tools and processes for effective budgeting, cost allocation, and real-time monitoring. It enables organizations to identify inefficiencies, track spending trends, and implement best practices such as rightsizing, reserved instances, and automation. Furthermore, FinOps promotes a culture of shared responsibility for cloud costs, ensuring that both technical and financial teams work together to drive cost optimization across departments. This approach allows enterprises to reduce waste, improve forecasting accuracy, and scale their cloud usage in a financially sustainable manner. As cloud spending continues to rise, FinOps is positioned to play an increasingly pivotal role in large-scale cloud cost management. By offering a structured framework for financial transparency and cost control, FinOps helps organizations achieve a balance between optimizing cloud costs and maintaining flexibility, performance, and innovation in their cloud environments.

A Conceptual Framework for the Adoption of Cloud Computing in a Higher Education Institutions

Today, with significant improvements of computing capacities, the world is witnessing significant technological advancements. Cloud computing especially, is increasingly becoming an advantageous tool, in developed countries especially, and these countries have invested substantially in cloud computing systems to enable the implementation of hi-tech advancements in many of their industries. On the other hand, in developing and underdeveloped countries, the adoption of cloud computing is still in the early stages; as can be observed, there has been some form of digital transformation of data systems in these countries. In Jordan, The factors affecting cloud computing adoption among higher education institutions were still underexplored, especially on the issues pertaining to cloud computing adoption. Therefore. Major factors with potential impact on user adoption of cloud computing were hence reviewed in this study. A conceptual framework of cloud computing adoption based on Extended Technology-Organizational-Environmental (TOE) framework by Tornatzky and Fleischer was proposed. The framework includes individual factors as the theoretical base for cloud computing adoption, to provide an inclusive comprehension on the factors that could affect behavioral intention and use of cloud computing.

A hybrid AI based framework for enhancing security in satellite based IoT networks using high performance computing architecture

IoT device security has become a major concern as a result of the rapid expansion of the Internet of Things (IoT) and the growing adoption of cloud computing for central monitoring and management. In order to provide centrally managed services each IoT device have to connect to their respective High-Performance Computing (HPC) clouds. The ever increasing deployment of Internet of Things (IoT) devices linked to HPC clouds use various medium such as wired and wireless. The security challenges increases further when these devices communicate over satellite links. This Satellite-Based IoT-HPC Cloud architecture poses new security concerns which exacerbates this problem. An intrusion detection technology integrated in the central cloud is suggested as a potential remedy to monitor and detect aberrant activity within the network in order to allay these worries. However, the enormous amounts of data generated by IoT devices and their constrained computing power dose not allow to implement IDS techniques at source and renders towards typical central Intrusion Detection Systems (IDS) ineffectiveness. Moreover, to protect these systems, powerful intrusion detection techniques are required due to the inherent vulnerabilities of IoT devices and the possible hazards during data transmission.During the course of literature survey it is revealed that the research work has been done to detect few types of attacks by using the old school model of IDS. The computational expensiveness in terms of processing time is also an important parameter to be considered. This work introduces a novel Embedded Hybrid Deep Learning-based intrusion detection technique (EHID) based on embedded hybrid deep learning that is created specifically for IoT devices linked to HPC clouds via satellite connectivity. Two Deep Learning (DL) algorithms are integrated in the proposed method to improve detection abilities with decent accuracy while considering the processing time and number of trainable parameters to detect 14 types of threats. It segregates among the normal and attack traffic. We also modify the conventional IDS approach and propose architectural change to harness the processing power of central server of cloud. This hybrid approach effectively detects threats by harnessing the computing power available at HPC cloud along with leveraging the power of AI. Additionally, the proposed system enables real-time monitoring and detection of intrusions while providing monitoring and management services through HPC using IoT-generated data. Experiments on Edge-IIoTset Cyber Security Dataset of IoT & IIoT indicate improved detection accuracy, reduced false positives, and efficient computational performance.

Cloud Security and Data Privacy: Safeguarding Critical Infrastructure During Application Migration

The migration of applications to cloud environments is a critical process for organizations seeking enhanced scalability, agility, and cost-efficiency. However, this transition introduces significant security and privacy challenges that must be addressed to protect sensitive data and critical infrastructure. This paper explores best practices and advanced techniques for safeguarding these assets during application migration, with a particular focus on cloud security and data privacy. Cloud security encompasses policies and measures that protect cloud-based systems, while data privacy focuses on safeguarding personal and sensitive information. As organizations increasingly migrate critical infrastructure to the cloud, it is essential to assess vulnerabilities and ensure data protection from unauthorized access and breaches. This paper highlights the importance of robust security practices, compliance with regulatory standards, and the role of emerging technologies in supporting a secure and seamless migration process. Through real-world case studies, it offers actionable insights for organizations to secure their cloud environments, maintain compliance, and mitigate risks associated with cloud adoption. Keywords: Cloud Security, Data Privacy, Application Migration, Critical Infrastructure, Cloud Adoption, Data Protection, Security Policies, Regulatory Compliance, Cloud Computing, Cloud Risks.

Cloud Technologies Revolutionizing Global Education: A Technical Case Study

The article offers a thorough examination of how cloud technologies, by offering strong answers to persistent problems in the distribution of educational information, have radically changed the face of global education. Through the strategic application of cutting-edge technologies like serverless architectures, dynamic resource allocation systems, and content delivery networks, educational institutions have successfully developed accessible and scalable learning environments that cater to a wide range of international audiences. It painstakingly details how these technology deployments have produced notable advancements in several areas, such as improved system performance, cost-effective cost structures, and improved user experiences. The article describes how educational institutions have successfully integrated cloud infrastructure, emphasizing their capacity to effectively handle growing user demands while continuously preserving remarkable levels of system availability and performance metrics. Beyond the obvious operational advantages, the article offers a thorough roadmap for educational institutions looking to use cloud technology by identifying important factors for future deployments and delivering priceless insights into technical implementation tactics. It also highlights the transformational potential of cloud technology in contemporary education by highlighting how careful cloud adoption has improved the overall quality of educational delivery in addition to resolving technical issues. The article's conclusions are especially important because they offer practical advice for educational establishments around the globe, showing how strategic technology adoption can both solve operational issues and further learning goals in a setting that is becoming more and more digital.

Cyber Security Threats and its Analysis

The rapid evolution and adoption of cloud computing have revolutionized the way organizations store, manage, and access data. However, this transition has introduced a wide array of cybersecurity threats, posing significant challenges to data integrity, confidentiality, and availability. This review paper provides a comprehensive analysis of cybersecurity threats in cloud environments, with a focus on understanding their implications and exploring effective mitigation strategies. The study categorizes cloud security threats into several key areas, including data breaches, account hijacking, insecure interfaces, and denial of service (DoS) attacks IEEE Xplore MDPI. It highlights how shared technology vulnerabilities and malicious insider threats exacerbate these risks, especially in multi-tenant cloud architectures IEEE Xplore. These issues are further compounded by regulatory and compliance challenges, requiring organizations to navigate complex legal landscapes while maintaining robust security protocols MDPI. To counter these threats, various technical and organizational measures are examined. Technical solutions such as encryption, intrusion detection systems, and blockchain technologies offer promising avenues for securing data and preventing unauthorized access IEEE Xplore IEEE Xplore . Additionally, adopting zero-trust architectures and leveraging artificial intelligence for real-time threat detection are identified as emerging trends that could significantly enhance cloud security MDPI. The paper also discusses the role of policy frameworks and regular security audits in fostering a culture of security awareness. Emphasis is placed on the need for a collaborative approach involving governments, academia, and industry stakeholders to develop innovative, scalable, and resilient security solutions. In conclusion, while cloud computing offers unparalleled benefits in terms of scalability and efficiency, its security challenges require a multifaceted approach. This review underscores the importance of continuous innovation in cybersecurity measures to safeguard against evolving threats, ensuring that cloud environments remain a reliable and secure platform for organizations worldwide..

Cloud-Enabled Innovation in Retail: Transforming Inventory Management and Demand Forecasting

This comprehensive article explores the transformative impact of cloud computing technologies on the retail industry, examining core platform solutions, implementation strategies, and future trends. The article investigates how cloud integration has revolutionized inventory management, demand forecasting, and operational efficiency across the retail sector. Through detailed case studies of major retailers, the research demonstrates the substantial benefits of cloud adoption in areas such as real-time data processing, predictive analytics, and supply chain optimization. The article encompasses technical considerations, best practices, and emerging technologies, providing insights into successful implementation strategies and operational guidelines. The article further evaluates the integration opportunities and standardization initiatives shaping the future of retail cloud computing, highlighting the critical role of artificial intelligence, blockchain, IoT, and advanced analytics in driving industry innovation.

The Integration of Large Language Models in Financial Services: From Fraud Detection to Generative AI Applications

This comprehensive article examines the transformative impact of artificial intelligence in the financial services sector, focusing on the evolution from traditional applications to advanced AI systems. Through systematic analysis of implementation frameworks and regulatory considerations, this article demonstrates the sector's technological leadership, evidenced by a 56% higher AI implementation success rate compared to other industries. The article reveals how Large Language Models have revolutionized customer interactions, achieving 92% query resolution accuracy and 89% improvement in user engagement. Documentation and compliance processes have been transformed through AI automation, demonstrating an 82% improvement in real-time compliance monitoring and 89% enhancement in automated reporting accuracy. Financial institutions' established regulatory frameworks and mature governance structures have enabled superior technology integration, with an 82% cloud adoption rate and 85% risk management effectiveness. The article’s analysis of software development infrastructure shows significant advancement, with a 73% improvement in deployment frequency and 82% enhancement in code quality metrics. The article highlights how financial institutions' robust regulatory expertise provides a significant competitive advantage, demonstrated by 89% security compliance and 76% stronger compliance protocols. Drawing from comprehensive industry analyses and empirical evidence, this article contributes to the growing body of literature on AI implementation in regulated industries while providing practical insights for organizations balancing innovation with compliance requirements.

The Negative Impacts of Cloud Migration and Redesign of Hybrid Clouds: The mitigation techniques

Cloud computing has revolutionized the IT landscape by offering scalable, efficient, and cost-effective solutions. However, migrating to the cloud and redesigning hybrid cloud architectures can come with significant challenges. While the benefits of cloud adoption are well-documented, the negative impacts and issues must also be considered to ensure a smooth transition and sustainable operation. Keywords: SOA, Cloud computing, Mitigation techniques, Cloud adoption

Cloud Computing: Evolution, Challenges, and Future Prospects

Cloud computing has redefined how individuals and organizations interact with computational resources. Providing scalable, flexible, and cost-efficient solutions has become a cornerstone of digital transformation in various sectors, including healthcare, education, finance, and artificial Intelligence. This paper examines the evolution of cloud computing, its architecture, service delivery models, benefits, challenges, and emerging trends. It also highlights critical aspects of cloud adoption and the implications of recent technological advancements. The study utilizes authentic sources to offer a holistic view of cloud computing's current and future trajectory.

CSPM for Healthcare: Securing Electronic Health Records (EHR) and Ensuring HIPAA Compliance

Cloud Security Posture Management (CSPM) has emerged as a critical solution for healthcare organizations navigating the complexities of digital transformation and cloud adoption. This comprehensive article examines how CSPM tools help healthcare providers secure Electronic Health Records (EHR) while maintaining HIPAA compliance in cloud environments. The article explores key challenges in maintaining data privacy and security requirements, evaluates the role of CSPM in automated security assessment and compliance monitoring, and presents real-world case studies demonstrating successful implementations. Additionally, it investigates future directions in CSPM development, including artificial intelligence integration and enhanced data protection features, providing insights into the evolving landscape of healthcare security management.

Proactive monitoring and security in cloud infrastructure: leveraging tools like Prometheus, Grafana, and HashiCorp Vault for Robust DevOps Practices

This study investigates proactive monitoring and security within cloud infrastructure, focusing on implementing tools like Prometheus, Grafana, and HashiCorp Vault to enhance operational resilience and data security. As cloud adoption grows, so does the complexity of maintaining secure systems amidst ever-evolving threats and regulatory pressures. By integrating these tools within DevOps workflows, organizations can achieve real-time monitoring, visualization, and secure access management, significantly strengthening their cloud environments. A systematic literature review was conducted, examining each tool's unique capabilities, the limitations they address, and the potential for combined functionality to offer a layered security approach. The findings reveal that while these tools are invaluable for preemptively detecting threats and managing access, they require continuous refinement to meet scalability, compatibility, and compliance demands. This study concludes that a proactive, integrative approach utilizing Prometheus for data collection and alerting, Grafana for visualization, and HashiCorp Vault for secret management fosters robust cloud security practices. Recommended strategies include adopting zero-trust architectures, utilizing automated compliance tools, and integrating sustainability principles in cloud management to ensure both security and operational efficiency. Ultimately, this study emphasizes the need for adaptive security frameworks that evolve alongside technological advancements, positioning organizations to safeguard their digital assets and respond to emerging threats with agility and confidence.

Adoption of Cloud Computing in Academic Libraries in Lagos and Ogun States

Abstract This study explored the adoption of cloud computing in selected academic libraries. A survey research design was used for the study. A sample size of 152 was randomly selected from 250 academic librarians and library officers for the study. A questionnaire was used as the instrument of data collection. The data collected were analyzed using descriptive statistics (frequency count, percentage, mean, and standard deviation). The study found that library personnel are aware of cloud computing technology which could enhance effective implementation of the technology in the libraries in Lagos and Ogun States. Furthermore, cloud computing technology was adopted for the purpose of collaboration among staff and users, accessibility, storage digital resources etc. The study revealed a significant number of challenges to adopting cloud computing, such as lack of financial resources, concern for data security, lack of in-house experts, and, most importantly, staff resistance to change. The study recommended that library management address the lack of in-house experts and improve internet connectivity to ensure effective implementation of cloud computing technology.

Determinants of Cloud Computing Adoption Amongst SMEs in the UK Technology Sector

Cloud computing has become a transformative technological model for businesses, offering flexibility, scalability, and cost-effectiveness. However, the embracing of cloud solutions amongst small and medium-sized enterprises (SMEs), particularly in the UK technology sector, has been varied. This research aims to identify and examine the significant determinants influencing cloud computing adoption among SMEs in this dynamic industry. Grounded in the Technology-Organization-Environment (TOE) framework, the study employed a quantitative methodology, surveying 102 SMEs across software development, IT services, and emerging technologies. The findings revealed that technological factors (IT infrastructure compatibility, technical skills), organizational factors (top management support, financial resources, employee readiness), and environmental factors (competitive pressures, industry practices) significantly influenced cloud adoption decisions. Notably, organizational factors emerged as the strongest predictor, followed by technological factors, while environmental factors had a weaker but significant impact. The regression model states that 59% of the variance in cloud adoption is explained. The study provided insights into the evolving priorities and shifts in factor importance between early and later cloud adopters. Recommendations are offered for SMEs, cloud providers, policymakers, and industry bodies to facilitate successful cloud migration journeys for technology SMEs in the UK. This research contributes to the understanding of the factors driving the adoption of cloud computing.

FACTORS INFLUENCING CLOUD COMPUTING ADOPTION : TECHNOLOGY, ORGANIZATIONAL AND ENVIRONMENT (TOE) FRAMEWORK APPROACH

The latest technology that must be applied to prepare for modernity is based on information technology. This technology is not only used by large companies (enterprises) but can also be utilized by Micro, Small and Medium Enterprises (MSMEs). One of the utilization of technology is cloud computing by Micro and Medium Enterprises (MSMEs) which is increasingly widespread. This is reasonable because cloud computing technology provides benefits with a simpler process and relatively low costs. In line with this, this study aims to determine the factors that influence the adoption of cloud computing in MSMEs. An integrated model was developed based on the Technology, Organization, and Environment (TOE) framework. This research data was obtained through a structured survey questionnaire given to 150 MSME owners/managers in the Soloraya Region. The results obtained through SEM-PLS analysis show that factors such as organizational readiness and competitor pressure have a significant effect on cloud computing adoption in MSMEs in the Soloraya Region. However, factors of relative advantage, complexity, and top management support were found to have no significant effect on cloud computing adoption. This research provides a theoretical contribution to the expansion of the TOE Framework.

The Growing Trend of Cloud-Based Data Integration and Warehousing

This article examines the growing trend of cloud-based data integration and warehousing solutions in response to the exponential growth of data generation and the need for scalable, flexible analytics capabilities. It explores the key drivers behind cloud adoption, including data volume increases, demand for real-time insights, cost efficiencies, and advanced analytics requirements. The benefits of cloud solutions are discussed, such as scalability, cost-effectiveness, agility, and enhanced analytical capabilities. The article also addresses challenges like data security, vendor lock-in, and performance issues, providing best practices for successful implementation. Emerging trends in automated ETL processes, machine learning integration, and multi-cloud approaches are highlighted, showcasing the evolving landscape of cloud-based data management. Through analysis of market projections, case studies, and industry statistics, this comprehensive overview equips organizations with the knowledge to leverage cloud technologies to transform their data management capabilities and gain competitive advantages in the digital age.

ETL vs ELT: Evolving Approaches to Data Integration

This article explores the evolution of data integration approaches, focusing on the transition from Extract, Transform, Load (ETL) to Extract, Load, Transform (ELT) methodologies. It examines the characteristics, advantages, and challenges of both approaches in the context of modern data management requirements. The article discusses the factors driving the shift towards ELT, including cloud adoption, big data growth, real-time analytics demands, and the rise of data lake architectures. Additionally, it presents best practices for implementing ELT, covering areas such as data cataloging, version control, data quality checks, performance optimization, and security compliance. Through analysis of industry trends and real-world use cases, the article provides insights into why organizations are increasingly adopting ELT frameworks and the considerations involved in this transition.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

Behavior intention in coping cloud adaptation

This study investigates the indirect impact of perceived technology complexity on the adoption of cloud platforms by data scientists in Brazil. Specifically, it examines the mediating role of perceived work-related opportunities and behavioral intentions, as well as the use of exploration-to-innovate coping strategies. The research employs an online questionnaire survey and uses Confirmatory Factor Analysis, SEM, and OLS to analyze the data. The findings suggest that a serial mediation effect focused on behavioral aspects can better explain the adoption of cloud computing technology. The study identifies the need for further investigation into the potential moderating effects of voluntariness and self-efficacy on the relationship between perceived technology complexity and cloud platform adaptation. Moreover, incorporating other exogenous constructs such as new adaptation strategies, job outcomes, and job satisfaction may provide a more comprehensive understanding of the factors influencing technology adoption in the context of data science. The study highlights that reducing technology complexity can lead to increased adoption, better user experience, better retention rates, more innovation possibilities, and better competition position. However, additional training and support requirements for data scientists can increase the cost and time required for onboarding and maintenance. A cloud platform that is easy to use can enable remote work and collaboration, opening up opportunities for job growth and flexible work arrangements. This can be particularly beneficial for Brazilian professionals who have strong innovation skills but limited training and support. By transcending geographic barriers and infrastructure limitations, a user-friendly cloud platform can help bridge the digital divide. On the other hand, a complex cloud platform can widen the digital divide, particularly in developing countries, exacerbating existing inequalities. The research contributes to theory building by providing a serial mediation framework that better explains technology adaptation phenomena by combining coping theory and technology acceptance model.

Banking in the cloud: mapping big tech’s global digital technology networks

ABSTRACT Witnessing the inroads of big tech into finance, this paper maps the growing dependence of incumbent banks on public cloud infrastructures. Although ‘cloud interfacing’ has become crucial amid platform finance, the intersections between what we conceptualise as global digital technology networks and global financial networks have received little attention. We mobilise social network analysis to map: (1) the geographically variegated patterns of public cloud adoption in banking and their geopolitical implications; (2) the corporate networks supporting banks’ cloud migration; and (3) the urban networks of command and control undergirding cloud banking, connecting established financial centres with tech valleys old and new.