CSIT - 2024 рік

Постійне посилання зібрання

https://elar.khmnu.edu.ua/handle/123456789/15905

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Перегляд CSIT - 2024 рік за Автор "Lysenko, S."

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  • Ескіз
    Документ
    Advanced methods for maintaining and managing the life cycle of cloud environments: survey
    (Хмельницький національний університет, 2024) Lysenko, S.; Bondaruk, O.
    Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods
  • Ескіз
    Документ
    Method for creating svm classifier for data analysis on FPGA
    (Хмельницький національний університет, 2024) Lysenko, S.; Shpuliar, Y.
    The paper explores the use of SVM classifier method for data analysis on FPGA, which, despite its effectiveness, may face challenges related to limited resources and data processing speed. In this context, there is a need to develop new methods for integrating SVM classifiers with high-performance computing hardware. The increasing demand for speed and energy efficiency requires new approaches to implementing machine learning methods. One of the key tools for data classification and analysis is the Support Vector Machine (SVM), widely used in business, science, medicine, and many other fields. Developing an efficient and optimized method for creating SVM classifiers for FPGA requires further research and development, as existing methods may be suboptimal in terms of speed and FPGA resource utilization. The article provides an overview of known hardware solutions to this problem, proposed in the current scientific literature. Additionally, the effectiveness of combining hardware and software components to achieve significant acceleration of the data analysis process is discussed. The article emphasizes the need for further research and improvement to fully realize the transformative potential of machine learning classification methods. The work resulted in the development of a new, specialized, and optimized hardware accelerator based on FPGA for the Support Vector Machine (SVM) method using convex optimization (CO) on embedded platforms. The proposed embedded architectures are designed to be universal, parameterized, and scalable. This means that these embedded solutions can accommodate different datasets of varying sizes and can be implemented on various embedded platforms, including those equipped with the latest FPGAs. They are also capable of handling both linear and nonlinear discrimination across multidimensional datasets.