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What is Computational IoE Grid Resources, and Collaboration, Model, and Design

Chapter 11
Internet of Behaviors Implementation in Organizational Contexts
The structure of a GRID, which may be used in the processes of engineering optimization and design search. Our system will offer seamless access to an intelligent knowledge library, a variety of cutting-edge optimization and search tools, industrial-grade analytical programs, as well as distributed computing and data resources. We lay a key emphasis on the open standards technologies that have to be leveraged for the system to be effectively executed, and we present some specific instances of how these technologies are now being put to use in real-world settings.
Published in Chapter:
Investigation of High-Performance Computing Tools for Higher Education Institutions Using the IoE Grid Computing Framework
Nilamadhab Mishra (VIT Bhopal University, India), Getachew Mekuria Habtemariam (Addis Ababa Science and Technology University, Ethiopia), and Anubhav De (VIT Bhopal University, India)
Copyright: © 2023 |Pages: 25
DOI: 10.4018/978-1-6684-9039-6.ch011
Abstract
The IoE Grid Computing Framework will be used to look into high-performance computing tools for higher education institutions. The primary and secondary data from higher education organizations are used to make the model and architecture. So, using a computational IoE grid and the supported toolkits, a model of the event was made. To see how well resources and related algorithms work, they are put to the test in different situations, such as by confirming the number of resources and users with different needs. To determine the level of demand for high-performance computing tools, data were gathered from particular colleges and universities. Before the data was fed into the system, preprocessing tasks like handling missing values and choosing features were completed. The findings demonstrate that these traits and the requirement for high-performance computing resources in higher education institutions are strongly related.
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