This article analyzes the characteristics of security redundancy structure and cloud computing in the dynamic management system of business management teaching resources. Taking the ZC system as an example, based on t...
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This article analyzes the characteristics of security redundancy structure and cloud computing in the dynamic management system of business management teaching resources. Taking the ZC system as an example, based on the integration of blockchain decentralization technology, the overall scheme of a cluster-based ZC service system based on cloud computing is proposed. Develop and design custom consensus algorithms and smart contracts that align with the foundational principles of decentralized blockchain technology. By implementing a reputation-based evaluation system for nodes within the network, the influence of malicious nodes on the consensus procedure can be effectively mitigated. This paper investigates the potential risk of single-point failures in the security redundancy architecture of the dynamic management system for business administration teaching resources, particularly within the ZC subsystem, which is critical to ensuring system integrity. Create a blockchain-based program and propose a deployment method for a minimal system using containerization, while also introducing an attack tree analysis consensus algorithm to address potential information security risks. Assess the impact of factors such as block size and the number of nodes in the cluster on the throughput, consensus latency, and cycle stability of the consensus algorithm, providing a comprehensive evaluation of system performance. The proposed ZC cluster in this study complies with the SIL4 security standard, offering superior reliability compared to traditional ZC systems in terms of security and fault tolerance. Regarding system availability, the PoTH consensus algorithm, as proposed, exhibits an average delay of 4.33 ms with a standard deviation of 0.173 ms across multiple trials. The algorithm's cycle stability ensures smooth operation of up to 40 trains while remaining within the maximum delay threshold of 100 ms.
Internet of Things (IoT) becomes a prominent sensing paradigm between the devices. Its evolution in the global digital increases extensively in various domains. For IoT application's sensors are the primary source...
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Internet of Things (IoT) becomes a prominent sensing paradigm between the devices. Its evolution in the global digital increases extensively in various domains. For IoT application's sensors are the primary source for generating data. These collected data are subject to the identification and detection of outliers/anomalies. The massive volume of data generation makes anomaly detection a complex and challenging task. The anomalies affect the data accuracy and data quality. In this paper, the k-NN classifier is proposed for enhancing classification accuracy. K-NN follows a non-parametric strategy and is one of the known classification algorithms. In the proposed system, k-NN is utilized to perform classification or regression with estimations of their k nearest neighbors. The proposed system consists of three major processes such as data preprocessing, classification, visualization. This study explores the utilization of 5G connectivity and cloud computing infrastructure for outlier detection in IoT data streams. Leveraging the K-Nearest Neighbors (KNN) classifier, our methodology focuses on efficiently identifying anomalies in IoT data. By integrating 5G connectivity for real-time data transmission and cloud-based machine learning for scalable analysis, we demonstrate a robust framework for outlier detection in IoT environments. The Experimental work with the proposed method is carried out using training and observation is tabulated with respective classes. As a result, on the three metrics, the proposed k-NN proves its efficiency is far better than the others, with an average of 98.4% of accuracy.
This paper presents a new method for analyzing ghost paths for camera lens design using a method that predefines ray orders and uses cloud-based parallel processing. Our method enables comprehensive ghost analysis of ...
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This paper presents a new method for analyzing ghost paths for camera lens design using a method that predefines ray orders and uses cloud-based parallel processing. Our method enables comprehensive ghost analysis of complex optical systems with multiple reflections, which were previously considered computationally impractical. The performance evaluation demonstrated the successful processing of over 4.4 billion rays through complex optical paths, completing calculations within practical time constraints.
The increase of national attention has also promoted the growth of the scale of sports health industry. However, for ordinary people who lack professional knowledge, intuitive data cannot make correct sports planning....
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The increase of national attention has also promoted the growth of the scale of sports health industry. However, for ordinary people who lack professional knowledge, intuitive data cannot make correct sports planning. Therefore, aiming at the problem that it is difficult for ordinary people to make correct exercise plan according to intuitive data, a personalized exercise plan generation system based on cloud computing is proposed. By analyzing the user's movement and physical data, the system uses cloud computing resources and machine learning algorithms to provide customized exercise recommendations for users. The key innovation of the research is the combination of improved random forest algorithm and reinforcement learning, while improving the performance of the algorithm on unbalanced sample sets. The results indicated that the accuracy of the improved random forest was 0.985 higher than that of the precision weighted random forest. The research algorithm was 9.04% higher on average than the original random forest algorithm and 2.71% higher than the accuracy weighted random forest algorithm. In terms of the accuracy of personalized motion scheme generation of motion software, the improved algorithm reached 95.05% at most, and its recall rate reached 83.46% at most. Compared with the existing sports software solutions, the research system can generate personalized sports programs more accurately, promote the development of the sports health industry and improve the national physical health level. The system can provide users with personalized sports suggestions, and utilize the powerful computing power of cloud computing to realize real-time processing and analysis of large-scale user data, providing users with timely sports feedback and suggestions.
To effectively reduce the dimension of cloud computing redundant data and shorten the time of dimensionality reduction, an algorithm for dimensionality reduction of cloud computing redundant data in the internet of th...
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Resource management has become a major concern in dealing with performance and fairness in recent computing servers, including a wide variety of shared resources. To achieve high-performing and efficient systems, both...
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Resource management has become a major concern in dealing with performance and fairness in recent computing servers, including a wide variety of shared resources. To achieve high-performing and efficient systems, both hardware and software engineers must be thoroughly trained in effective resource management techniques. This paper introduces the GRE master course (Spanish acronym for Resource Management and Performance Evaluation in cloud and High-Performance Workloads), which is being offered since Fall 2023. The course is taught by instructors with broad research expertise in resource management and performance evaluation. Subjects covered in this course include workload characterization, state-of-the-art resource management approaches, and performance evaluation tools and methodologies used in production systems. Management techniques are studied both in the context of HPC and cloud computing, where resource efficiency is becoming a primary concern. To enhance the learning experience, the course integrates theoretical concepts with a wide set of hands-on tasks carried out on recent real platforms. A real cloud virtualized environment is mimicked using typical software deployed in production systems such as Proxmox Virtual Environment. Students learn to use tools such as Linux Perf and Intel Vtune Profiler, which are commonly employed by researchers and practitioners to carry out typical tasks like performance bottleneck analysis from a microarchitectural perspective. Overall, the GRE course provides students with a solid foundation and skills in resource management by addressing current hot topics both in the industry and academia. Student satisfaction and learning outcomes prove the success of the GRE course and encourage us to continue in this direction.
This study identifies the determinants of cloud computing adoption and its effect on the performance of Sri Lankan small and medium-sized enterprises (SMEs). The Technology-Organization-Environment (TOE) framework, Te...
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This study identifies the determinants of cloud computing adoption and its effect on the performance of Sri Lankan small and medium-sized enterprises (SMEs). The Technology-Organization-Environment (TOE) framework, Technology Acceptance Model (TAM), and individual context were used to derive the study variables. This quantitative cross-sectional study adopted items from previous validated studies. Google Form was employed to collect data, and 418 responses were received from Sri Lankan SMEs. Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4 and Artificial Neural Network (ANN) analysis via IBM SPSS 29 were used for data analysis. Based on the results, all hypotheses are confirmed except for one, and SME performance is significantly affected by cloud computing adoption. This study adds to the existing empirical evidence on cloud computing adoption by introducing an all-inclusive model that integrates the TOE, TAM, and individual factors. This demonstrates the effectiveness of the PLS-SEM/ANN hybrid methodology in analysing the determinants of cloud computing adoption. The significance of top management as a factor is highlighted by providing training and education to employees. Managers can benefit from this result by improving cloud computing adoption among SMEs in Sri Lanka. This is the first study of its kind in Sri Lanka, integrating the TOE, TAM, and individual variables and using a hybrid methodology combining PLS-SEM and ANN.
Amongst the most transformational technologies nowadays, cloud computing can provide resources such as CPU, memory, and storage over secure internet connections. Due to its flexibility and resource availability with g...
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Amongst the most transformational technologies nowadays, cloud computing can provide resources such as CPU, memory, and storage over secure internet connections. Due to its flexibility and resource availability with guaranteed QoS, cloud computing allows comprehensive business and research adoptions. Despite the rapid development, resource management remains one of the significant challenges, especially handling task scheduling efficiently in this environment. Task scheduling strategically assigns tasks to available resources so that Quality of Service (QoS) metrics are effectively related to response time and throughput. This paper proposes an Enhanced Harris Hawks Optimization (EHHO) algorithm for scheduling cloud tasks to mitigate the common limitations found in existing algorithms. EHHO integrates a dynamic random walk strategy, enhancing exploration capabilities to avoid premature convergence and significantly improving scalability and resource allocation efficiency. Simulation outcomes reveal that EHHO minimizes makespan by up to 75%, memory usage by up to 60%, execution time by up to 39%, and cost by up to 66% compared to state-ofthe-art algorithms. These benefits demonstrate that EHHO can optimize resource allocation while being highly scalable and reliable. Consistent performance over various stacks such as Kafka, Spark, Flink, and Storm further evidences the superiority of EHHO in handling complex scheduling challenges in dynamic cloud computing environments.
Most of the information available on cloud computing is either highly technical, with details that are irrelevant to non-technologists, or pure marketing hype, in which the cloud is simply used as a selling point. Thi...
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ISBN:
(数字)9780262334129
ISBN:
(纸本)9780262529099
Most of the information available on cloud computing is either highly technical, with details that are irrelevant to non-technologists, or pure marketing hype, in which the cloud is simply used as a selling point. This book, however, explains the cloud from the user's viewpoint. The author explains what the cloud is, when to use it (and when not to), how to select a cloud service, how to integrate it with other technologies, and what the best practices are for using cloud computing. A simple and basic definition of cloud computing from the National Institute of Science and Technology is considered: a model enabling ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. Thus businesses, individuals and communities can harness information technology resources usually available only to large enterprises. This, as the author demonstrates, represents a paradigm shift for businesses and individuals alike. In additon, the book considers the contractual, legal, financial, security and risk related aspects of adopting and migrating to the cloud. cloud patterns are examined in terms of five deployment models; and a cloud computing maturity model is derived to align the use of cloud computing with best practices.A unique aspect of the book is that it provides innovative constructs that affect the way cloud computing shall be viewed and used in the future. In particular, it addresses novel concepts for cloud computing: cloud cells, or specialist clouds for specific uses; the personal cloud; the cloud of things and services; and cloud service exchanges.
As the need for digital content increases almost daily, preserving this content for university libraries, especially in the least developed and developing nations, becomes a challenge, resulting in the loss of this da...
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As the need for digital content increases almost daily, preserving this content for university libraries, especially in the least developed and developing nations, becomes a challenge, resulting in the loss of this data. Even though most of these libraries are automated and adopt one digital preservation strategy or the other, library users need more assurance of continued access to these digital objects. Thus, there is a need for university libraries to adopt emerging technologies such as cloud computing for proper digital preservation. Owing to the insufficiency of knowledge on the relationship between technology and humans, taking account of human capabilities and how they work in harmony with technology like cloud computing called for a study of this nature. This study investigates the determinants of cloud computing adoption in university libraries, focusing on technological and human factors. A quantitative survey design technique was adopted, and purpose sampling was used. Data were gathered and analyzed from 398 staff from Information and Communication (ICT) directorates, e-library, and institutional digital repositories units of universities as decision-makers for IT adoption. Results show that all the variables under technological and human factors except for complexity have a positive relationship with the intention to adopt cloud computing. This study gives decision-makers like information scientists, librarians, and information technologists' insights into how the two factors contribute to the successful adoption of cloud computing. It also contributes to the knowledge of technology adoption literature, adding more knowledge to the theory-building, especially in the context of university libraries.
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