Cloud Computing Research Articles

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

Cloud-Based Service for Fingerprint Image Matching

Fingerprint matching is one of the most important services that related to the issue of identifying individuals, as it can be used to verify the validity of contracts, documents or in other security fields such as criminal investigations. Fingerprints are also one of the most common and accurate forms, as they are easy to obtain, it also has a unique features and cannot be identical to any individuals. The proposed service exploits the advantages provided by cloud computing, such as ease of use, availability of the service anytime and anywhere, and no need to own expensive devices with high specifications, in addition to being low cost. In this service, all fingerprint image preprocessing operations and images enhancement, in addition to the matching, verification and decision-making process are done with the help of tools which provided by cloud computing environment. While the client's role in this service is limited to feeding the system with the fingerprint to be matched. The experimental results proved that this service has achieved high accuracy in matching fingerprint images, in addition to greatly facilitating the matching process for the user, and it can be used anytime and anywhere thanks to the cloud environment facilities adopted by this service.

Leveraging AWS and Java Microservices: An Analysis of Amazon\'s Scalable E-commerce Architecture

Abstract: This article presents a comprehensive case study of Amazon's journey in building a robust and scalable e-commerce platform capable of handling millions of daily transactions while maintaining high availability and performance. We examine the critical architectural decisions that facilitated Amazon's transition from a monolithic structure to a microservices-based architecture, leveraging Java and various AWS cloud services. The article explores key components of Amazon's scalable infrastructure, including the implementation of DynamoDB for high-performance database needs and the use of Elastic Load Balancing to ensure fault tolerance. We analyze the challenges encountered during this transformation and the solutions developed to address them. The article also discusses the resulting improvements in scalability, reliability, and cost-efficiency that have contributed to Amazon's position as the world's largest online retailer. Our findings provide valuable insights into best practices for architecting large-scale, cloud-native e-commerce platforms and offer implications for future developments in the field of distributed systems and cloud computing

Data is the fuel of generative artificial intelligence

TThe aim of the article is to present the generative technology of artificial intelligence and a general analysis of its achievements and technological limitations. In turn, the hypothesis is as follows: data is the main factor ensuring the development of generative AI. The identified research niche includes stimulants of this development, the need to clarify the concept of generative AI and the indication of the most frequently used sources and methods of obtaining data. At the beginning, the concept and genesis of artificial intelligence were explained, which was necessary to move on to further considerations regarding the analyzed generative artificial intelligence. The self-learning mechanisms used by this type of artificial intelligence are discussed, consisting mainly in the analysis of specific data sets, thanks to which computers learn things that have not been known before. The advantages of generative artificial intelligence were also indicated, including increased productivity, greater efficiency and high creativity. Part of the attention was also paid to the limitations of this technology, including inaccurate and fuzzy output information, uncritical analyzes and often biased conclusions, copyright infringement and high requirements for memory and computing power of computers. Two factors have been identified that have recently stimulated the development of generative intelligence, i.e. the growing computing power of computers and easy access to large data sets. The main subject of considerations are large data sets, dynamically and uncontrollably self-replicating in Internet servers. Today, data resources are collected very efficiently, including: in Big Data, Cloud Computing and Internet of Things technologies, and their use is almost cost-free and publicly available. It was enough to improve the technology of artificial neural networks, expand multi-layer machine learning and create deep learning for generative intelligence to gain enormous momentum and become a symbol of the beginning of the 21st century. The whole investigation was summarized with a synthetic ending.

Optimal robust configuration in cloud environment based on heuristic optimization algorithm

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system’s actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10−6, indicating the accuracy of our solution.

Research on Chinas Economic and Trade Pattern under the Internet Technology Era

In the era of the Internet, Chinas economic and trade models have undergone profound and transformative changes, reshaping the landscape of global commerce. This paper aims to explore the critical role that internet technology has played in driving Chinas economic development while also delving into the future directions and possibilities for further growth. By focusing on Alibaba and JD.com as primary case studies, the paper examines how these industry leaders have leveraged advanced technologies such as cloud computing, cross-border e-commerce, and electronic logistics to revolutionize their operations and expand their global reach. The study not only highlights their innovative approaches and development experiences but also addresses the societal challenges that have emerged or been exacerbated by the rapid expansion of internet technology. Understanding these challenges is essential for formulating strategies to mitigate their impact and ensure long-term, sustainable growth. The significance of this research lies in its potential to provide valuable insights and practical references for small and medium-sized enterprises (SMEs) in China. By learning from the successes and challenges of leading enterprises, SMEs can navigate the complexities of the Internet era more effectively, contributing to the sustainable and healthy development of Chinas economy in an increasingly digital world.

Efficient Task Scheduling for Large-scale Graph Data Processing in Cloud Computing: A Particle Swarm Optimization Approach

To fulfil the requirements of task scheduling for processing massive amounts of graph data in cloud computing environments, this thesis offers the LGPPSO method, which is based on Particle Swarm Optimisation. The LGPPSO algorithm considers the task’s overall structure when initialising numerous particles in order to broaden the search range and raise the likelihood of finding an approximation optimal solution. We evaluated it in large-scale simulation trials with 100 performance-heterogeneous virtual machines (VMs) using both randomly generated and real application graphs, and evaluated its effectiveness against the CCSH and HEFT algorithms. The experimental findings demonstrate that, in both randomly generated graphs and real graph structure applications, significantly reducing the scheduling duration of large-scale graph data is the LGPPSO algorithm. For randomly generated 200 and 400 node tasks, respectively, the scheduling length is shortened by approximately 8.3% and 9.7% on average when compared to the HEFT algorithm. The LGPPSO technique minimises the scheduling length for actual graphical structure applications by an average of 14.6% and 16.9% for the Gaussian 100 and 200 node examples, respectively.

Optimizing data management in healthcare: Lessons from clinical trials and beyond

This study comprehensively explores the optimization of data management in healthcare by drawing on lessons learned from clinical trials and extending these insights to broader healthcare contexts. The study's purpose is to examine the challenges, strategies, and technological innovations that are essential for effective data management in modern healthcare systems. Utilizing a detailed literature review, the study identifies key methodologies from clinical trials that have successfully enhanced data integrity, accuracy, and security, such as the implementation of electronic data capture (EDC) systems and rigorous data governance frameworks. These methods are crucial in managing the increasing volume and complexity of healthcare data. The study's findings reveal that big data analytics and artificial intelligence (AI) are pivotal in transforming healthcare data management, enabling personalized and precise patient care. However, these technologies also introduce challenges related to data security, interoperability, and ethical considerations, particularly concerning patient privacy and the transparency of AI-driven decision-making processes. In conclusion, the study emphasizes the need for continuous innovation and improvement in healthcare data management practices. Recommendations include the integration of blockchain technology, cloud computing, and real-time data from wearable devices to address existing challenges and enhance data management capabilities. The study advocates for a balanced approach that prioritizes patient privacy, ethical governance, and regulatory compliance to ensure that technological advancements contribute positively to patient outcomes and healthcare efficiency.

Research on the Impact of Digital Financial Technology on the Business Model Transformation of Commercial Banks

This paper explores in depth the broad impact of digital financial technologies on the business models of commercial banks and analyzes in detail the transformation strategies that commercial banks should adopt in response to these changes. With the rapid development of cutting-edge technologies such as big data, cloud computing, artificial intelligence, and blockchain, digital finance has become an irreversible trend in the financial industry, which has not only changed the way financial services are provided, but also reshaped the market competition pattern of commercial banks. This paper first expounds the impact of digital financial technology on the traditional business model of commercial banks, including the reduction of market entry threshold, the change of competition focus and the innovation of risk management model. Then, the paper focuses on the specific strategies of commercial bank business model transformation, including technological innovation, organizational structure optimization, process management improvement, talent training and introduction, and strengthening cooperation and win-win situation. Through the introduction of the actual case analysis, it shows the successful practice and lessons of commercial banks in the business model transformation. Finally, this paper concludes that the key to the successful transformation of commercial banks' business model lies in the deep integration of technology and business, as well as the ability to flexibly respond to market changes and customer needs. Only in this way can commercial banks remain competitive and achieve sustainable development in the era of digital finance.

Digitalization for ensuring innovative development of the enterprise

This paper examines the influence of digitalization on the innovative development of enterprises. The integration of digital technologies serves as a key driver for product, process, marketing, and organizational innovations, which collectively contribute to competitive advantages and the enhancement of business process efficiency. Core avenues of digitalization encompass automation, data analytics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and digital platforms. These technologies equip firms with the capacity to swiftly adapt to market fluctuations, optimize productivity, and foster resilience in an increasingly dynamic economic environment. The strategic implementation of digitalization extends beyond operational enhancements, enabling the reconfiguration of traditional business models. By leveraging digital tools, enterprises are able to deliver personalized customer experiences, enhance decision-making through real-time data analytics, and achieve more efficient coordination across value chains. Additionally, the adoption of digital platforms facilitates rapid scaling and the exploration of novel revenue channels through the provision of innovative digital services. In the context of a globalized economy, where adaptability and technological sophistication are critical, digital transformation emerges as a decisive factor in sustaining long-term competitiveness. Furthermore, this paper addresses the intersection of digitalization and sustainability. A growing number of enterprises are employing digital technologies to decrease resource consumption and mitigate environmental impact, aligning their practices with the increasing emphasis on corporate social responsibility and the principles of a circular economy. As businesses continue to pursue digital transformation, their capacity to innovate, maintain competitive positioning, and contribute to sustainable development will play a pivotal role in shaping the future trajectory of the business landscape.

A Novel Hybrid Intrusion Detection Framework Leveraging Machine Learning and Flower Pollination Optimization

The exponential growth in technologies such as cloud computing, smart devices, virtualization, and the Internet of Things (IoT) has generated over four hundred zettabytes of network traffic data annually. This surge necessitates robust cybersecurity strategies to protect information from intrusions, which can result in significant financial losses. Reducing security risks requires the use of data analytics and machine learning to derive insights and make informed decisions based on network data. This study introduces the Flower Pollination Optimization (FPO) algorithm for feature selection to enhance the performance of several classifiers on the UNSW-NB15 dataset. We evaluated four classifiers: Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors (KNN) in two scenarios: without feature selection and with FPO-based feature selection. The results demonstrate significant improvements in classifier performance with FPO, with QDA achieving the highest accuracy of 99.16%. Comparative analysis with recent studies highlights the superior performance of our approach, setting a new benchmark in intrusion detection. This research underscores the essential role of effective feature selection in improving the accuracy and reliability of Intrusion Detection Systems (IDS), particularly in IoT environments.

The role of digital transformation in enhancing clean energy startups' success: An analysis of it integration strategies

This review paper explores the pivotal role of digital transformation in enhancing the success of clean energy startups. It delves into how integrating digital technologies such as IoT, AI, big data, and blockchain can foster innovation, improve scalability, and boost operational efficiency. The paper also examines various IT integration strategies, including cloud computing, data analytics, and smart grid technologies, and their implementation models, highlighting the challenges and potential solutions. Additionally, it discusses the impact of digital transformation on market competitiveness and provides strategic recommendations for startups to leverage these technologies effectively. The paper concludes by predicting future trends in digital technologies and outlining policy implications to support the digital transformation of the clean energy sector.

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Survey of Mobile Cloud Computing Security and Privacy Issues in Healthcare

Quantum Computing and Cloud Technologies: The Next Frontier in Computing Power

Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

IN TERMS OF ORGANIZATIONAL BEHAVIOR A BIBLIOMETRIC RESEARCH OF LEADERSHIP 4.0 WHICH IS A REFLECTION OF INDUSTRY 4.0

The Industry 4.0 revolution refers to the digital transformation of industry. The significance of the Industry 4.0 revolution is further enhanced by the advent of new technologies concomitant with this revolution. The technologies of Industry 4.0 include artificial intelligence, the Internet of Things, big data, and cloud computing. Organizations that adopt these technologies can adapt to the Industry 4.0 revolution and gain a competitive advantage. By means of Industry 4.0 technologies, it is possible to save resources used in production and to produce products with better quality and different features. Organizations that fail to leverage these technologies will be unable to reap the benefits of the Industry 4.0 revolution, potentially leading to a disadvantage in the competitive landscape. It is therefore crucial for organizations to adopt Industry 4.0 technologies. It is imperative that employees possess the requisite knowledge, skills, and abilities to utilize Industry 4.0 technologies. Those in leadership positions are able to exert influence and direct the knowledge, skills, and abilities of employees. Consequently, the pivotal factor in the implementation of Industry 4.0 technologies within an organizational context is leadership. The concept of Leadership 4.0 posits that leaders possess distinctive advanced knowledge, skills, and abilities that enable them to influence employees and facilitate the utilization of industry 4.0 technologies within the organization. Leadership 4.0 serves as an indispensable conduit for the transfer of industry 4.0 technologies within organizational settings. Leadership 4.0 plays a pivotal role in preparing organizations for the advent of industry 4.0. Consequently, the successful integration and utilization of industry 4.0 technologies within organizations hinges upon the effectiveness of Leadership 4.0. This study aims to elucidate the impact of Leadership 4.0 on employee outcomes. In accordance with the objective, a bibliometric analysis of the studies on Leadership 4.0 in the Web of Science (WOS) database was conducted using the VOSviewer program.

Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation strategy that aims to ensure EV information security. In a cloud computing environment, users do not want other users and cloud providers to have access to their personal information, which is precisely the problem that secure multi-party computing (SMPC) can solve. At present, full homomorphic encryption (FHE) can solve the problem of user data privacy preservation in cloud computing and big data environments and can realize the whole encryption process. Therefore, a more reasonable charging station selection scheme is provided under the computation of privacy preservation strategies incorporating the FHE-SMPC method. The effectiveness and implementation feasibility of the designed privacy preservation strategy in practical applications is verified through testing and comparative analysis. The results show that the developed strategy can significantly reduce the risk of privacy leakage with limited communication resources and computation time consumption. The results provide new perspectives and methodologies for interactive privacy preservation between EVs and charging stations, with application potential.

Cloud Computing Security Issues, Challenges, and Solutions

Cloud computing has revolutionized the way agencies manage and deliver their IT offerings by means of imparting scalable, bendy, and fee-effective solutions. However, with these good sized blessings come complicated protection demanding situations. The allotted nature of cloud environments, coupled with multi-tenant infrastructures, offers new risks that require sturdy and adaptive security frameworks. This paper goals to provide an in-intensity exploration of the primary security problems in cloud computing, the underlying challenges companies face in securing cloud services, and the solutions designed to mitigate these dangers

Development of Detection and Mitigation of Advanced Persistent Threats Using Artificial Intelligence and Multi-Layer Security on Cloud Computing Infrastructure

This research proposes a novel approach for detecting and mitigating Advanced Persistent Threats (APTs) in cloud computing infrastruc ture, offering more comprehensive protection compared to previous methods. By integrating detection and mitigation, this study addresses the shortcomings of prior research that focused solely on detection. Based on the conducted research, Artificial Intelligence (AI) detected Cross-Site Scripting (XSS) attacks with an accuracy of 0.9951, SQL Injection (SQLI) at 0.9964, and Remote Code Execution (RCE) at 0.9876. In trials against new attacks, the detection success rates reached 70% for XSS, 98% for SQLI, and 100% for RCE. During the deployment phase, the system successfully identified 23.040 out of 108.394 requests as XSS attacks, 2.684 out of 128.750 as SQLI attacks, and 1.135 out of 46.450 as RCE attacks. The detection and mitigation methods were directly tested on cloud server experiencing APT attacks. The daily attacks on the server reached 1.980, with 663.000 requests. Additionally, the number of attacks directed at authentication or sensitive pages reached 17.913.701. Attack mitigation was tested through seven layers of security, including DNS Protection, Config Server Firewall (CSF), OWASP ModSecurity, HTTP middleware, data filter or sanitizer, template engine, and manual mitigation successfully blocking million of persistent attacks. The DNS protection layer successfully mitigated 59,000 out of a total of 19 million requests. The CSF layer mitigated 173 sources IP of DDoS attacks. The ModSecurity layer mitigated 17,916,204 attacks. All attacks were successfully mitigated before reaching the HTTP Middleware stage or next layer. The use of NIST 2.0 standards helps manage security risks through identification, protection, detection, response, and recovery. Test results indicate that this multi-layered system is more efficient and effective in detecting and mitigating attacks compared to traditional methods. However, the complexity of implementation and maintenance poses challenges that must be addressed. This research significantly contributes to a more adaptive and sustainable cybersecurity strategy.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.