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The NIST definition of cloud computing
... Cloud computing refers to the delivery of computing resources over the Internet on a pay-asyou-go basis. As defined by the National Institute of Standards and Technology (NIST), cloud computing encompasses five essential characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service (Mell & Grance, 2011). ...
... Characteristics such as On-demand self-service, broad network access, resource pooling, and rapid elasticity. On-demand self-service allows users to provision and manage computing resources autonomously without human intervention (Mell & Grance, 2011). Broad network access ensures that services are accessible over the internet from various devices. ...
... Public cloud services provide scalability, cost-efficiency, flexibility, accessibility, on-demand self-service, and broad network access, making them attractive for organizations looking for scalable, cost-effective, and flexible solutions for their computing needs (Armbrust et al. 2010). Hybrid cloud solutions offer a combination of benefits from both private and public clouds, including scalability, flexibility, security, data control, cost optimization, and seamless integration (Mell & Grance, 2011). ...
This study investigated cloud computing and finance industry in Nigeria. In this study, sixty (60) questionnaires were administered to the selected sample of financial institutions in Benin City. The data were analysed using the ordinary least square regression technique. The hypotheses stated in the study were validated. The findings in the study revealed that private cloud, public cloud and multi cloud were positively related to the finance industry. Furthermore, hybrid cloud and cloud security were negatively related to the finance industry. The study concludes that an increase in the use of private cloud, public cloud, multi cloud services enhances the operations of financial institutions in Nigeria, while an increase in the use of hybrid cloud and cloud security results in a decline in the operations of financial institutions in Nigeria. The study recommends that financial institutions in Nigeria should engage the service provider of private cloud, public cloud, hybrid cloud, multi cloud or cloud security services to enhance the protection of corporate data. The government should enact laws and statutory regulations that may compel financial institutions to protect corporate data.
... In the early stages of cloud computing and pre-cloud environments, data protection methods are relatively straightforward, relying on basic encryption techniques such as symmetric encryption.One of the most widely used methods is the Data Encryption Standard (DES), a symmetric encryption algorithm that was adopted in the 1970s.The DES has become the official encryption standard for many years, providing a foundational level of security for sensitive data (Schneier, 2007).However, as the processing power of computers increased and attackers became more sophisticated, DES was eventually deemed inadequate because of its relatively short key length, which made it vulnerable to brute-force attacks.By the late 1990s, as computing power continued to grow, DES was replaced by a more secure Advanced Encryption Standard (AES), which offered longer key lengths and greater scalability (Mell, 2011). Although encryption provides a strong foundation for securing data, traditional access control methods are also essential.Early access control models were predominantly role-based or rule-based, relying on user authentication mechanisms, such as passwords, to restrict access to data.These mechanisms worked well in smaller, isolated environments, but became increasingly inadequate as systems scaled and data began to be shared across multiple platforms and geographical locations, as seen in the early days of cloud computing (Zissis & Lekkas, 2012).The limitations of these traditional methods are exacerbated by the dynamic nature of cloud environments, where data are frequently transferred between distributed systems, thereby creating new attack surfaces (Subashini & Kavitha, 2011). ...
... Similarly, small and medium enterprises (SMEs) may struggle with the costs and technical expertise required to deploy and maintain advanced access control systems, such as ABAC or IAM frameworks, which can be particularly complex to configure and monitor. Identity and Access Management (IAM) IAM frameworks are vital for managing user access to resources in cloud environments.These systems enforce authentication and authorization policies, often including multifactor authentication (MFA), password management, and real-time monitoring.IAM is crucial for reducing the risk of breaches caused by human error, which remains a leading cause of data vulnerability (Mell, 2011). However, the implementation of IAM systems can be challenging for SMEs.The resource-intensive nature of deploying IAM, particularly when integrated with various cloud applications and on-premises systems, can be a barrier.SMEs may also face difficulties maintaining the required level of expertise in managing IAM systems, especially if they lack dedicated IT security teams.Furthermore, while MFA enhances security, it can also add friction to the user experience, which may lead to resistance among employees or end users. ...
... Backup and disaster recovery (DR) solutions are vital for ensuring data resilience in the event of breach, failure, or disaster.Cloud providers offer automated, geographically redundant backup systems that replicate data across multiple locations, ensuring that the data remains accessible even if one site is compromised. However, implementing robust backup and DR solutions presents challenges, particularly in multi-cloud or hybrid environments where ensuring seamless synchronization and recovery across platforms can be complex and costly (Mell, 2011). ...
This article explores the evolving techniques for data protection in cloud computing, focusing on the growing challenges of safeguarding data in an increasingly interconnected digital landscape marked by frequent breaches and stringent regulatory demands. It examines the strategies and technologies employed in cloud environments, such as encryption, access control mechanisms, and compliance measures, to secure data, both in transit and at rest.This study highlights the significance of robust security frameworks, with particular emphasis on zero-trust architectures and confidential computing, which have gained prominence as critical approaches for minimizing cyber risks.Zero-trust models, which prioritize continuous verification and dynamic access controls identified as essential for enhancing data protection, whereas confidential computing solutions aimed at protecting sensitive data during processing, even in the cloud.Additionally, this article explores the role of artificial intelligence (AI) and machine learning (ML) in advancing data protection by enabling predictive threat detection and automated responses to security incidents.The findings underscore that, while security measures have become increasingly sophisticated, the continuous integration of AI-driven analytics and adaptive security frameworks is crucial for mitigating evolving threats and meeting the challenges posed by regulatory frameworks.This study offers insights into how these emerging technologies shape data protection strategies and provides valuable perspectives for industry practitioners and policymakers to navigate the security landscape.
... Defined by the National Institute of Standards and Technology (NIST), cloud computing is "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" (Mell & Grance, 2011). Its essential characteristics-on-demand selfservice, broad network access, resource pooling, rapid elasticity, and measured serviceenable organisations to leverage IT infrastructure as a utility, eliminating the need for significant capital investment in on-premises hardware. ...
... I. Data Encryption: Ensures data confidentiality both at rest and in transit. Encryption mechanisms protect sensitive data from unauthorized access (Mell & Grance, 2011). ...
... The rapid shift to cloud computing has heightened the risk of data breaches, as the shared nature of cloud resources complicates traditional security models. Effective data protection strategies include encryption, access controls, and comprehensive monitoring of data access and usage patterns (Mell & Grance, 2011). Moreover, organisations must foster a culture of security awareness among employees, as human error remains a significant factor contributing to data breaches . ...
In an era marked by the rapid digitization of data and the widespread adoption of cloud computing, the security of sensitive information has emerged as a paramount concern for organizations and individuals alike. This dissertation explores the integration of cryptographic techniques into cloud computing and big data security, emphasizing the need for robust protective measures against increasing threats. The study employs a comprehensive methodology based on the Waterfall Model to design and implement a functional web-based cryptographic prototype utilizing the Advanced Encryption Standard (AES) in Galois/Counter Mode (GCM). User-friendly interfaces developed with HTML, CSS, and JavaScript facilitate seamless encryption and decryption processes, allowing users to engage with cryptographic practices effectively. Through empirical user testing, the findings reveal that the prototype significantly enhances users' confidence in handling sensitive data while simultaneously fostering a deeper understanding of the importance of data security. Users expressed satisfaction with the application's usability, highlighting the effectiveness of intuitive design and clear instructions in promoting user engagement and compliance with best practices. The research underscores the significance of integrating cryptographic solutions within broader security frameworks to address regulatory challenges, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). In conclusion, this study contributes to the existing body of knowledge by illustrating how practical applications of cryptography can mitigate risks associated with data breaches and unauthorized access in cloud environments. It advocates for ongoing user education and the adoption of advanced security measures, thereby paving the way for a more secure and resilient digital landscape in the face of evolving cybersecurity threats.
Keywords: Data Protection, Cryptography, Cloud Computing, Big Data Security, Web-Based Portal, User Authentication.
... SaaS provides fully functional software applications hosted on the cloud, which users can access through web browsers. It offers convenience and cost efficiency, removing the need for installations or maintenance (Mell & Grance, 2011). Deployment architectures further define how cloud resources are utilized. ...
... The dynamic nature of cloud infrastructures further complicates detection efforts. Continuous changes, including scaling, shifting workloads, and frequent updates, can make it difficult to establish a consistent baseline for normal behaviour (Mell & Grance, 2011). ...
The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.
... Wireless E-Governance is an emerging model of remote computing that utilizes these infrastructures to build dynamic networks of collaborative activities that extend across regulatory boundaries. However, the open and global architecture of cloud computing introduces significant security risks, which must be addressed to ensure the security and integrity of E-Government systems (Sifaleras & Petridis, 2019;Mell, 2011). The paper's outline is as follows: Section 2 covers related works; section 3 discusses e-government and its requirements; Section 4 outlines the key components of an enhanced e-government framework using cloud computing; and Section 5 the proposed system, discusses the use of cloud computing platforms in e-governance systems. ...
... To visually represent the components and requirements of this enhanced e-governance framework, imagine a diagram with the following elements, as shown in Table 3 (Chinese Academy of Cyberspace Studies, 2019; Ali, 2021): Monitors use and optimizes expenses with cloud management tools Effective resource allocation cuts operating costs. Figure 1 shows how merging cloud computing and data management may meet the key needs of e-governance, offering a complete, secure, and scalable solution for modern digital governance (Mell, 2011). Figure 2 illustrates the workflow. ...
This study proposes a novel way for electronic government organizations to create and deploy computer systems. The recommended solution uses Amazon Web Services (AWS) S3 and Python's Pandas module to handle and analyze citizen data in a safe, scalable cloud. Data, security, and flexibility are major issues with e-government apps. It shows that cloud computing may increase digital governance's reliability, security, and efficiency, offering new alternatives to conventional paradigms. The utilization of cloud computing approaches to overcome traditional network restrictions and create a more flexible and efficient digital framework makes this research unique. According to the study, AWS S3 and Pandas can handle enormous datasets, improve data security, and streamline public service delivery. This strategy promotes service delivery and citizen interaction while improving e-governance's technological competence. According to the paper, online computing's scalability, efficiency, and security make it revolutionary in public administration. It gives states an organizational structure to embrace cloud-based apps, showing how technological improvements may improve government efficiency and efficacy, benefiting residents and the public. Emphasizes electronic governance by proposing a new cloud-based computing architecture for public administration. It enables governments to build more durable and adaptive electronic systems, enabling online governance advances.
... 4. How do edge computing and federated learning contribute to scalable IoT analytics, and how can they be integrated with cloud-based solutions? 5. What are the real-world applications and case studies that demonstrate the effectiveness of scalable ML models for IoT data analytics in cloud environments? ...
... Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [5]. Cloud computing offers several key advantages for IoT data analytics: 5. Advanced services: Cloud providers offer a wide range of analytics, machine learning, and artificial intelligence services that can be leveraged for IoT data processing and analysis. ...
The Internet of Things (IoT) has revolutionized data collection across various domains, generating massive amounts of heterogeneous data at unprecedented rates. This surge in data volume and velocity presents both opportunities and challenges for data analytics. Cloud computing environments offer a promising solution for processing and analyzing IoT data due to their scalability and resource elasticity. This paper presents a comprehensive review and analysis of scalable machine learning models designed for IoT data analytics in cloud environments. We explore the synergies between IoT, cloud computing, and machine learning, discussing the challenges of processing IoT data at scale and the advantages of cloud-based solutions. The paper examines various machine learning algorithms and architectures optimized for cloud deployment, including distributed learning frameworks, federated learning, and edge-cloud collaborative models. We also present case studies demonstrating the application of these models in real-world IoT scenarios, such as smart cities, industrial IoT, and healthcare. Our findings highlight the importance of scalable machine learning models in extracting valuable insights from IoT data and the role of cloud environments in enabling efficient, large-scale data analytics.
... Data security is a critical concern, especially with the increasing use of cloud technology in sectors such as e-commerce, where customer confidential information is at risk. Protecting data involves advanced encryption techniques, strict access controls, and robust security protocols to defend against unauthorized access, data breaches, and data loss during transmission and storage [26,34,35]. Reliability and performance issues fall mostly into the medium safety class or importance, with seven cases belonging to this category and three qualified to be high risk. ...
The adoption of cloud computing has become essential for SMEs, especially in the e-commerce industry, as it has the potential to improve the efficiency of operations. However, the existing literature indicates significant gaps, including a general lack of comprehensive research focused on cloud computing adoption for SMEs and very limited research on the Kingdom of Saudi Arabia (KSA) context. This study aimed to fill these gaps by evaluating the current status of cloud computing adoption by SMEs in KSA and identifying the challenges facing its implementation in the e-commerce sector. To achieve these objectives, the Technology-Organization-Environment (TOE) framework was employed, analyzing the technological, organizational, and environmental factors that affect cloud service adoption. This study incorporates insights from government agencies, service providers, and SMEs to provide a holistic view. Through a mixed-method approach, combining qualitative and quantitative data, this study offers a detailed understanding of the cloud computing landscape in KSA, providing valuable insights for stakeholders and policymakers.
... -Adopt secure cloud services and ensure physical and logical security measures for on-premises storage. [28,30] ...
As Artificial Intelligence (AI) continues to permeate various sectors such as healthcare, finance, and transportation, the importance of securing AI systems against emerging threats has become increasingly critical. The proliferation of AI across these industries not only introduces opportunities for innovation but also exposes vulnerabilities that could be exploited by malicious actors. This comprehensive review delves into the current landscape of AI security, providing an in-depth analysis of the threats, challenges, and mitigation strategies associated with AI technologies. The paper discusses key threats such as adversarial attacks, data poisoning, and model inversion, all of which can severely compromise the integrity, confidentiality, and availability of AI systems. Additionally, the paper explores the challenges posed by the inherent complexity and opacity of AI models, particularly deep learning networks. The review also evaluates various mitigation strategies, including adversarial training, differential privacy, and federated learning, that have been developed to safeguard AI systems. By synthesizing recent advancements and identifying gaps in existing research, this paper aims to guide future efforts in enhancing the security of AI applications, ultimately ensuring their safe and ethical deployment in both critical and everyday environments.
... The term "cloud computing" refers to a model that allows users to have easy, everywhere, anytime access to a shared pool of configurable computing resources (such as servers, networks, storage, apps, and services) that can be quickly provisioned and released with little intervention from service providers or management [2]. Scalability, affordability, flexibility, and accessibility are four of the main reasons why cloud computing is chosen for dataintensive applications and analytics [3]. ...
With more and more organisations looking to get important insights from their massive data sets, data analytics has grown in importance in today's data-driven world. One effective strategy for improving data analytics is the combination of cloud computing, AI, and ML. This research paper explores the synergistic relationship between these technologies and their collective impact on data analytics. The paper begins by providing an overview of cloud computing, AI, and ML, highlighting their individual strengths and how they can be leveraged in the context of data analytics. It then delves into the integration of these technologies, discussing the benefits, challenges, and best practices for effective implementation. The study examines several use cases and real-world applications where the integration of cloud computing, AI, and ML has led to improved data analytics, such as predictive modeling, anomaly detection, and decision support. The paper also presents a comparative analysis of different cloud-based AI and ML platforms, evaluating their features, performance, and suitability for various data analytics scenarios. Furthermore, the research explores the ethical considerations and regulatory implications surrounding the use of these integrated technologies, addressing issues like data privacy, algorithmic bias, and transparency.The article finishes by suggesting next steps for businesses interested in using cloud computing, AI, and ML for improved data analytics, as well as by describing current trends and possible developments in this space.
... High availability in cloud environments is often achieved through load balancing and failover mechanisms. Studies have shown that cloud-based HA solutions provide seamless failover and load distribution, ensuring that applications remain available even during hardware or software failures [2]. The use of auto-scaling features allows cloud-based systems to handle varying loads, further enhancing availability. ...
Customer Relationship Management (CRM) systems are central to modern business operations, facilitating the management of customer data, enhancing customer interactions, and driving business growth. Given their critical role, ensuring the availability and resilience of CRM systems against potential failures and disasters is imperative. This paper focuses on implementing disaster recovery (DR) and high availability (HA) for CRM systems deployed on Linux virtual machines (VMs) within cloud infrastructures such as AWS, Azure, and Google Cloud. Leveraging the capabilities of Linux VMs, this research explores methodologies for achieving robust DR and HA, including load balancing, failover clustering, data replication, and automated backup strategies. Through case studies and experimental results, the paper demonstrates effective strategies for maintaining system continuity and minimizing downtime. Key challenges such as network latency, data integrity, security risks, and cost management are addressed with practical solutions. The findings provide a comprehensive framework for IT professionals to implement DR and HA in cloud-based CRM systems, ensuring business continuity and data protection.
... The adoption of cloud services continues to grow exponentially due to the advantages it provides, including cost savings, increased efficiency, and enhanced accessibility. However, this shift towards cloud-based environments has also introduced a plethora of cybersecurity challenges that necessitate effective risk assessment strategies [1], [2]. ...
With the rapid expansion of cloud computing, the need for robust cybersecurity measures has become paramount. As organizations increasingly migrate their data and applications to the cloud, they encounter numerous cybersecurity risks that threaten the integrity, confidentiality, and availability of their information. Traditional risk assessment methods often fall short in addressing the dynamic and complex nature of cloud environments. This paper explores a novel approach to cybersecurity risk assessment in cloud computing using machine learning techniques. We propose a comprehensive framework that leverages machine learning algorithms to detect, predict, and mitigate potential cybersecurity threats. The framework incorporates various supervised and unsupervised learning models, including decision trees, support vector machines, and neural networks, to analyze large datasets and identify patterns indicative of security breaches. Our approach also includes feature selection methods to optimize the performance of these models by focusing on the most relevant risk factors. We conducted extensive experiments on publicly available cloud security datasets, which demonstrated the efficacy of our machine learning-based risk assessment framework in identifying threats with high accuracy and minimal false positives. The results indicate that our approach significantly outperforms traditional risk assessment techniques in terms of speed, scalability, and adaptability to evolving threat landscapes. This study contributes to the field by providing a scalable and efficient solution for enhancing cybersecurity in cloud environments. It highlights the potential of machine learning to revolutionize how we assess and manage cybersecurity risks, offering a proactive stance against emerging threats. Future work will focus on refining the model by incorporating real-time data and exploring advanced machine learning techniques such as deep learning and reinforcement learning to further enhance its predictive capabilities.
... Cloud computing has achieved tremendous success on today's Internet, offering numerous opportunities for businesses and individuals by providing computing, storage, and networking infrastructure along with various services. According to the National Institute of Standards and Technology (NIST), cloud computing is defined as a paradigm that provides omnipresent remote access to pooled resources on demand [28]. A cloud data center is a vast collection of virtualized resources that are highly accessible and reconfigurable to accommodate dynamic workloads, supporting a pay-as-you-go pricing model [29,30]. ...
... The common online storage people usually utilize are under the cloud storage, wherein free data is given for consumers by the different platforms. To which, they are deciphered as public, private, community, and hybrid cloud storage dependent on its intended usage [23][24][25][26] . Despite the differences, its main purpose still remains the same, storage, development, sharing, and automation among users intent. ...
Cloud storage has been widely considered among developed and developing countries due to its ability to provide a platform for large data and information storage. Developing countries like the Philippines have started using this storage and have only since considered the free services. With the aim to understand utility for development and continuous patronage, there has been lacking evidence in the intention and actual use of cloud storages. The need for study is evident to promote and develop concrete strategies for cloud storage uptake, even if payment is needed for extra storage. This study analyzed the antecedents of actual use behavior of cloud storage in a developing country like the Philippines using a machine learning ensemble (MLE). With 616 valid responses, a total of 33,264 datasets were processed to analyze the actual use of cloud storage among Filipinos, measured using the integrated extended technology acceptance model and valence framework. With an average accuracy of 93% and 90% for the MLE considered, results have presented consistent output of voluntariness, subjective norm, perceived benefit, perceived usefulness, and perceived ubiquity to be contributing factors affecting actual use behavior. It could be posited that both personal and professional usage of cloud storage has been considered by users. In addition, due to people’s readiness to use technology nowadays, the adoption of which is relatively convenient for them. Evident from the findings, further technological infrastructure is needed to be enhanced in the country for a more positive continuous intention. Therefore, the application of the integrated framework may be used and expanded for other technology utilities in different countries. Lastly, practical and managerial insights were built on the results to provide strategies and development needed for marketing, utility, and application.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-80676-9.
... According to the National Institute of Standards and Technology (NIST), cloud computing allows users to access a shared pool of reconfigurable computing resources, including networks, servers, storage, apps, and services, on-demand those can be swiftly allocated and deallocated with minimal management effort, or service provider engagement (Mell, 2011). Cloud computing can be implemented in public, private, or hybrid configurations, which provide distinct degrees of control, security, and data sharing among organizations (Gorelik, 2013). ...
This study addresses the pressing need to integrate cloud computing and artificial intelligence (AI) into Bangladesh’s tertiary accounting curriculum to keep up with modern information technology in a world where AI and cloud computing are increasing. Notwithstanding the rapid growth of Bangladesh’s economy and ICT sector, a substantial gap persists in incorporating AI and cloud computing into the accounting study curriculum. While these advances enhance efficiency, accessibility, flexibility, and economic viability, they face challenges related to safety, confidentiality, and insufficient investment in emerging technology. This article advocates for reforming the educational curriculum to incorporate emerging technology, hence fostering the development of future reskilled accounting professionals.
... Cloud services provide scalability and flexibility but also present unique security challenges (Dillon et al., 2010). The shared nature of cloud resources can lead to vulnerabilities that traditional security measures may not effectively address (Mell & Grance, 2011). Anomaly detection, which identifies deviations from expected behavior, is crucial for identifying potential threats in these environments (Chandola et al., 2009). ...
The rapid adoption of cloud computing has transformed the way organizations manage and store their data. However, this shift has also increased vulnerabilities to cyber threats. Anomaly detection is a critical component of cybersecurity frameworks, allowing for the identification of unusual patterns that may indicate security breaches. This paper presents a comprehensive framework for anomaly detection in cloud computing environments. It reviews existing methodologies, explores the integration of machine learning techniques, and discusses the challenges associated with implementing these systems. The proposed framework aims to enhance the cybersecurity posture of organizations by providing proactive detection of anomalies.
... Edge computing is a technology that allows data processing and storage to occur closer to the user, leveraging a network of devices at the edge of the Internet. Edge resources can be accessed within one hop of a wireless gateway, in contrast to the several hops to be taken before reaching the farthest cloud computing data center [9]. The modeling perspectives include offloading or replication tasks at the edge, mobile clients with different bandwidth and delay constraints accessing content close to them, and cooperative computing efforts amongst users, sensors, or actuators at the edge. ...
Emerging computing paradigms such as edge computing and cloudlet computing offer distributed computing resources in proximity to the sources and end users. By moving the processing closer to the data source and consumer, these novel paradigms improve the performance by decreasing latencies, and thus by enabling a faster and real-time provision of services. In addition, they can be used to offload massive amounts of raw data for processing on more powerful computing resources instead of sending all the data through the network towards the clouds, thus saving network bandwidth and mitigating congestion. These computing paradigms also enable more flexible service provision, as the resources can be easily scaled based on the demand in a productive and cost-effective way compared to the traditional cloud computing infrastructures. The rapid evolution of modern technologies has led to the emergence of a myriad of applications in different domains such as Industry 4.0, the Internet of Things, smart cities, health-care, and transportation. The typical processing approach in these applications involves data travel from sources to machines or servers for processing and then back to the end users. Since the data travel distance might be long depending on network and topological structures, traditional cloud computing is found to be insufficient to fulfil the performance, scalability, and security requirements of many modern applications. Therefore, there is an urgent need for novel computing paradigms which close the processing gap between data sources and consumers.
... A set of employee departments, including security functions, exists to protect cloud systems from internal and external attacks and from the consequences of operational errors. The insider is an individual or group representing a large organization to monitor data entities [24]. ...
This research focuses on the increasing security threats and challenges in cloud-based software systems and potential solutions. Topics talked about are data breaches, insecure APIs/shared technology vulnerabilities in a multi-tenant environment and insider threats. This research clearly indicates the glaring requirement for strong security practices, including encryption, Identity and Access Management (IAM), and continuous monitoring to diminish risks. In addition to the Typo3 example, we can see in case studies such as Capital One and Equifax the devastating effects of misconfigurations (CapitalOne) or unpatched vulnerabilities (Equifax). Other types of emerging threats are discussed, like ransomware, container vulnerabilities and supply chain attacks-all related to the dynamic nature of cloud environments as well. Moreover, a side-by-side comparison with the major Cloud Service Providers (CSPs)-AWS, Azure and Google Cloud.
... The DT of each production unit is established through the edge end, where the virtual and real mapping takes place, and thus integrated into the cloud server to form the DT of the entire production line. It is important to note that cloud computing is leveraged to enable ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (Mell et al., 2011), which can be efficiently provisioned and distributed with minimal administrative overhead. Meanwhile, edge computing is employed as a decentralized architecture driven by intelligent terminals and large networked devices in the Internet of Things, leading to reduced costs of computer components (Jain et al., 2016). ...
In the context of Industry 4.0, rapid changes in product demand make traditional flexible production systems oriented towards single-product production models unable to meet market competition. As a result, the convergence of mass production and customized production, known as hybrid production, has become the dominant mode of manufacturing. However, under the framework of hybrid production, if the flexible production system cannot meet the demands of different production modes at the same time, it will lead to scheduling chaos, low production efficiency and other related problems, so how to design the production system for hybrid production has become a problem that needs to be solved in flexible manufacturing factories. In this paper, we propose a digital twin-based modeling method for needle-flexible production lines for hybrid production, which consists of building a digital twin model of the production unit (DTPU) at the edge end through a digital thread communication framework, integrating it into a digital twin model of the production line (DTPL) at the cloud end, and providing a real-time data base for decision-making and analysis in manufacturing plants by uploading the real-time production status data of the production unit to the cloud end. At the same time, the decision-making information of the manufacturing factory passes the information to the production unit through the digital thread framework, and the production unit executes the corresponding processes to meet the demands of different models. The effectiveness of the method is verified through the application study of a specific model of PLC-controlled production line.
... The way that businesses especially small and medium-sized enterprises (SMEs) access and use technology has been completely transformed by cloud computing. Cloud computing as defined by the National Institute of Standards and Technology (NIST) offers shared pools of reconfigurable computing resources (e.g., networks, servers, storage, applications, and services) that require little management work and can be quickly provisioned and released (Mell and Grance 2011). Significant advantages of cloud computing for SMEs include reduced costs scalability improved collaboration and flexibility (Duan et al. 2012). ...
A route to greater cost-effectiveness competitive advantage and operational efficiency is being increasingly taken by Nepal’s SMEs through cloud computing. However, the question of how long-lasting cloud computing adoption is complex and influenced by several factors that extend beyond its initial installation. This study looks into the variables that affect Nepalese small and medium-sized enterprises (SMEs) ability to use cloud computing sustainably. This research attempts to identify key determinants of sustainable cloud computing adoption by analyzing a variety of factors including trust, security & privacy, sharing & collaboration, ease of use, and cost reduction. The research contextualizes its findings using the Technology Organization Environment (TOE) framework and the Diffusion of Innovations (DOI) theory. Based on firm size moderating these relationships the results show significant correlations between the identified factors and cloud computing sustainability. The results offer valuable perspectives for policymakers and business executives seeking to augment the adoption of cloud computing in Nepal.
... The users can also use their computers to access the cloud services anywhere and anytime without having control over the location of the resources. The users can even scale up or down the cloud services and pay for only the resources required or consumed [3][4][5][6]. In academic environments, cloud computing generally enables academic and non-academic staff, as well as students and researchers, to access resources and services provided by the cloud service vendors. ...
... According to NIST definition of Cloud Computing, "Cloud Computing is a model for enabling ubiquitous, convenient and on demand Network access to a shared pool of configurable computing Resources, such as Networks, Servers, Storage, Applications and services, that can be rapidly provisioned and released with minimal management effort or service provider interaction" [1]. It can be considered as an extension to Distributed Computing. ...
Cloud Computing is one of the growing domains of computer science where researchers across the world are working to get deep insight into this domain and to develop new algorithms in various aspects of computing to make it more easily accessible to the users. It provides a simple, web-based, architectural neutral and heterogeneous platform where the users can access the services and resources as per their requirements on a pay per use model with ease. Scheduling is a process through which one process is allowed to use the computing resources while another process is delayed because of unavailability of any resources. Scheduling mechanism makes the system more efficient, faster and fairer. In this paper, I have studied Space-Shared and Time-Shared Scheduling algorithm for Virtual Machine and different length tasks using CloudSim to evaluate the performance based on various parameters like turnaround time (TT), execution time (ET) and waiting time (WT). The objective behind this work is to analyze and evaluate the effectiveness of both algorithms with respect to scheduling the resources.
... The most widely recognized definition of cloud computing is provided by the US National Institute of Standards and Technology (NIST). According to NIST, "Cloud Computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" Mell and Grance (2011). Many organizations are fully aware of the many advantages and potential of cloud computing, which is thought to offer a revolutionary opportunity to fundamentally alter how business is done. ...
The increasing need for fast, secure and dynamic processing of information has necessitated the wide scale adoption of cloud computing. The educational sector especially the open and distance learning institutions are not left out of the need for distributed technology. This study presented a model that provided insight into the pre and post usage adoption variables associated with cloud computing adoption and performance in open and distance learning (ODL) setting. Partial Least Square (PLS) was adapted to test seven hypotheses on the causal relationship between the variables. Five out of the seven hypotheses were supported. The pre-usage and satisfaction oriented constructs showed more significant contributions in determining performance impact than the task technology fit oriented constructs. The study also provided insights on user preferences for decision making by educational institutions, service providers, business owners and government. Keywords: Cloud Computing, TUSPEM, Performance, Utilization, Cost, Ease Of Use
The technologies of artificial intelligence and cloud systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.
This chapter explores the technologies involved in certification systems designed to prevent food waste. First, it presents the state-of-the-art in IoT and blockchain technologies applied to the food supply chain, followed by a summary of the key certifications in this domain. The chapter then provides a comprehensive overview of how these technological innovations are being integrated to tackle the pressing challenges of the food industry, with a particular focus on food waste prevention. Finally, it concludes with insights and conclusions drawn from the analysis of these systems.
In today's digital landscape, organizations are increasingly leveraging hybrid multi-cloud architectures to gain the flexibility and control necessary for handling diverse workloads. A hybrid multi-cloud approach combines multiple public and private cloud environments, allowing businesses to tailor infrastructure choices to specific operational needs. While this architecture maximizes flexibility, it introduces complexities in ensuring consistent performance across diverse cloud environments, each with its own configuration, latency considerations, and cost implications. The challenges presented by this approach make performance engineering a vital component of maintaining efficient, scalable, and cost-effective multi-cloud solutions. Performance engineering in hybrid multi-cloud environments encompasses a range of methodologies designed to evaluate, optimize, and sustain the performance of applications and services. Key to this engineering effort are strategies for latency reduction, resource allocation, cost management, and maintaining resilience in dynamic cloud setups Effective performance engineering incorporates comprehensive monitoring and observability practices, load testing, and capacity planning to proactively address bottlenecks. Tools like Prometheus for monitoring, Grafana for visualization and JMeter for stress testing are instrumental in enabling detailed visibility into system performance, while AI-driven approaches offer predictive insights and automation capabilities to optimize resource use and reduce costs. This article provides an in-depth analysis of the principles and tools underpinning performance engineering within hybrid multi-cloud architectures. It discusses critical areas such as load balancing, automated scaling, latency optimization, and data management across cloud platforms, emphasizing how each component contributes to an integrated performance strategy. Real-world case studies are presented to illustrate the implementation of these strategies across various industries, from healthcare to finance, demonstrating how businesses leverage hybrid multi-cloud frameworks to enhance reliability and efficiency. The article also highlights emerging trends, such as edge computing and machine learning, which promise to drive further innovation in performance engineering by enabling more distributed and intelligent resource management. Through a combination of theoretical insights and practical applications, this article aims to equip IT professionals, system architects, and cloud engineers with the knowledge to address performance challenges in hybrid multi-cloud settings. By adopting a structured approach to performance engineering, organizations can better navigate the complexities of hybrid multi-cloud architectures, ensuring that their infrastructure supports evolving business needs while maintaining optimal service levels.
This study intends to assess the impact of digital transformation on public sector service delivery, concentrating on the State Information Technology Agency (SITA) in South Africa. The report examines critical obstacles obstructing the effective execution of digital transformation programs, including as antiquated infrastructure, procurement inefficiencies, and opposition to change. It also analyses the advantages of agile approaches in improving flexibility, responsiveness, and collaboration in digital projects. We utilised a qualitative study methodology, conducting semi-structured interviews with SITA personnel and analysing pertinent organisational documents. The dataset comprised interviews with personnel from several departments, together with performance and service delivery reports spanning from 2015 to 2022. The study's principal findings indicate that (i) agile methodologies markedly enhance the adaptability and efficiency of digital transformation initiatives; (ii) robust data governance and sharing practices promote transparency and accountability, albeit encountering obstacles such as data silos and inconsistent quality; and (iii) addressing challenges such as digital hesitancy and leadership deficiencies is crucial for cultivating a culture that encourages innovation and continuous improvement. The study ultimately offers strategic recommendations for tackling these difficulties and improving digital transformation initiatives inside SITA.
With the advancement of the cloud computing environment, the users’ expectations to gain better services significantly increased. One of the most prominent parts of cloud systems is the task scheduling concept in which its improvement can increase the users’ satisfaction as a consequence. Most of the published literature in this domain is extended to either a single-objective or bi-objective perspective. This paper presents a heuristic task scheduling algorithm for the optimization of makespan-cost-reliability (TSO-MCR) objectives. In addition, the users’ constraints are considered in the proposed optimization model. To this end, the task ranking approach, ignoring the unreliable processors, using Pareto dominance, and crowding distance approaches are utilized so the trade-off amongst potentially conflicting objectives is gained. To verify the effectiveness of the proposed TSO-MCR, its performance is compared with Multi-Objective Heterogeneous Earliest Finish Time (MOHEFT), Cost and Makespan Scheduling of Workflows in the Cloud (CMSWC), Hybrid Discrete Cuckoo Search Algorithm (HDCSA), and Multi-Objective Best Fit Decreasing (MOBFD) approaches. Since the comparative algorithms are bi-objectives, the multi-objective version of each is customized commensurate with the stated problem to prepare the same conditions. The simulation results prove that the proposed TSO-MCR significantly outperforms other state-of-the-art. It has 4.23, 8.93, 2.08, and 4.24% improvement against all counterparts in all 12 scenarios respectively in terms of makespan, total monetary cost, reliability, and the final score function incorporating all weighted objectives. It is worth mentioning that the comparison has been done on the datasets including both scientific workflow and random applications with different communication-to-computation ratio (CCR) values.
The digital paradigm has far-reaching implications for the global economy and for cross-border trade. Digital technologies are driving a new phase of global economic integration. With respect to international trade, it is generally acknowledged that the digital transformation changes what is traded, who trades and how trade is conducted. Data has emerged as a vital economic resource in the digital economy, leading to an exponential increase in global data exchange and a surge in cross-border data flows. The economic shift from tangible to intangible assets in a digital economy is resulting in the emergence of a digital paradigm for cross-border trade. This chapter is dedicated to illustrating the novel dimensions of cross-border economic activity in the era of digital globalisation. This chapter lays the groundwork for analysing the development of regulations for these new elements within a trade context and provides a background for analysing the nexus with data privacy regulations.
The economic benefits and scalability of public cloud computing are already undeniable due to recent advancements in the field; the only question that remains is cloud security. Despite the enormous benefits of moving their computing workload to the cloud, many organizations continue to show resistance to this change. Cloud security concerns are the most frequently mentioned cause. Organizations are concerned by a larger attack surface created by the worldwide accessibility of services in the cloud. The security and risk control set that enterprises can apply in the cloud is also often limited and impacted by the interoperability and support provided by the chosen Cloud Service Providers (CSPs), and organizations are often not allowed to extend their trusted security solutions they are already familiar with to the cloud. Yet, both traditional computing and cloud computing include security risks, and cloud risk is just as controllable as traditional IT risk. Secondary data obtained from Identity Theft Resource Centre (ITRC) database on cloud incidents from year 2020 to 2022 were analyzed in this study. To determine the primary underlying causes of cybersecurity events observed across the years covered by the available data, the study used trend analysis and descriptive statistics. The analysis shows that cloud incidents are not different from traditional incident and organizations can leverage existing capabilities already developed in traditional computing towards managing the cloud risk. Also, organizations need to take be proactive in their responsibility and take ownership of the risks. As the study shows, the majority of cloud incidents are caused by knowledge gaps and the cloud customer's inability to exercise due diligence and care in ensuring effective controls are put in place to stop prevalent attacks. Effective cloud training and adherence to the established cloud control matrix, like the CSA, would successfully lower risk to a reasonable level.
This study examines the critical role that Digital Information and Communication Technologies (DICT) play in developing critical thinking, creativity, collaboration, and digital literacy—skills that are essential for the 21st century. We recognize, however, that there are important challenges ahead, such as the need for ongoing training for educators and ensuring equitable access to technologies. A balanced approach that prioritizes pedagogy over technology and emphasizes the need to adapt curricula and teaching practices to maximize the potential of DICT is important, as highlighted by the study. Looking ahead, we propose strategies for effectively integrating emerging technologies, such as artificial intelligence and blockchain, into the development of 21st-century skills. We find that, while DICT has transformative potential for education, careful and contextualized implementation, informed by ongoing research and collaboration among all stakeholders in the educational ecosystem, is critical to the success of DICT. By harnessing the potential of digital technologies, this work contributes to advancing knowledge on how to effectively prepare students for the challenges of a rapidly evolving world.
The impact and Future of Cloud Computing in Modern IT Infrastructure involves various different topics and also sub-topics into it. The main purpose of the paper is to explore various different Challenges, Trends, Future Scope etc. in Cloud Computing. The main purpose of the Cloud Computing is to provide high-level of security to the uses so that they should feel safe to store there data. There are many different types in Clouds such as. Private Cloud, Public Cloud etc. According to the users they make the use of it.The study of Cloud Computing will definetly highlight the points such as security, Cost efficiency, scalability etc. By this people will also come to know about the trends in cloud computing, Uses , Impacts if it etc. This paper will provide you both economic and technical aspects. The use of technology has became very vast now-a-days. Many of the companies has there own Cloud to store there data. It makes use of the Private cloud so that it should not get mix up with different once and also should be secure. The modern world has made the use of cloud computing more and more.
As organizations adopt multi-cloud strategies to enhance operational agility, the integration of edge computing is emerging as a critical solution to address latency and redundancy challenges. This paper explores the architecture and design principles of hybrid data centers that seamlessly integrate cloud resources with edge nodes. It highlights the key considerations for ensuring redundancy, performance, and data sovereignty while managing distributed workloads. The study provides real-world case examples of hybrid data center deployments across different sectors, focusing on the role of software-defined networking (SDN) and network function virtualization (NFV). Furthermore, the paper identifies best practices and technological trends driving the adoption of hybrid architectures, including disaster recovery strategies, workload orchestration, and cross-platform security frameworks.
The financial services industry is undergoing a significant transformation driven by digital innovation and evolving customer expectations. Cloud computing has emerged as a pivotal technology enabling financial institutions to enhance operational efficiency, improve customer experiences, and maintain competitive advantage. This research article explores the adoption of cloud systems within the financial services sector, examining the drivers, benefits, challenges, and best practices associated with cloud integration. By analyzing current trends, regulatory considerations, and case studies of leading financial institutions, the paper highlights how cloud computing is reshaping the landscape of financial services. Additionally, it discusses future directions and technological advancements that will further influence cloud adoption in the industry, underscoring the critical role of cloud systems in fostering innovation and resilience in the financial sector.
In the rapidly evolving digital landscape, enterprises are increasingly reliant on diverse applications and systems to drive operations, enhance customer experiences, and maintain competitive advantage. Effective integration of these disparate systems is paramount to ensuring seamless data flow, operational efficiency, and agility. Integration Platform as a Service (iPaaS) has emerged as a pivotal solution, offering cloud-based platforms that facilitate the integration of applications, data, and processes across on-premises and cloud environments. This research article explores the future of enterprise integrations, emphasizing the role and adoption trends of iPaaS. It delves into the driving factors behind iPaaS adoption, the challenges enterprises face, and the emerging technologies shaping the future of integrations. Through an analysis of current trends, case studies, and expert insights, the paper highlights how iPaaS is poised to transform enterprise integration strategies, fostering innovation, scalability, and resilience in an increasingly interconnected world.
The COVID-19 pandemic turned the whole world “upside down” and it would be accurate to say that businesses have never
known a better time to rise and make those instant adaptations. Whether it was network operations, collaboration or service delivery, the
need for the switch was immense. COVID-19 made sure of it. Cloud platforms saw to it that many businesses that went remote whether
just for a couple of weeks or forever were up and running almost immediately. This paper critically examines the stark effect of COVID19 on businesses, focusing on how cloud computing solutions or platforms have facilitated the swift adaptation of businesses and what it
would have become for employees (and employers!) if not for this lockdown-friendly technology. This paper researches the advantages of
cloud platforms, provides a few interesting uses cases in different industry sectors, and gives visual examples of how businesses had to
switch to remote workforces during one of the most monumental events in their histories.
Cette étude explore l'importance de l'intégration de la prise de décision basée sur les données (data-driven decision making) et de l'analyse prédictive pour l'optimisation de la performance et la gestion proactive des risques dans les entreprises congolaises modernes. L’accent est mis sur la sous-utilisation des données dans les processus décisionnels et l’impact de cette lacune sur la performance des entreprises en République Démocratique du Congo (RDC). L'analyse examine comment la transformation digitale, à travers des outils tels que les ERP (Enterprise Resource Planning), les CRM (Customer Relationship Management) et les plateformes de Business Intelligence (BI), peut aider à améliorer l’efficacité opérationnelle, réduire les coûts, et anticiper les risques économiques, opérationnels et financiers. En outre, elle aborde les défis liés à l'adoption d'approches data-driven, notamment le manque d'infrastructures technologiques, la faible culture des données, et les résistances organisationnelles. À travers des études de cas d'entreprises africaines et congolaises ayant réussi à mettre en œuvre des stratégies fondées sur l'analyse des données, l'étude propose des recommandations pour renforcer les capacités technologiques des entreprises congolaises et stimuler l'innovation. Des initiatives gouvernementales et privées sont également soulignées pour appuyer la transformation numérique en RDC
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