The Role of Big Data, Generative AI, and Cloud Connectors in Advancing Education IT Solutions and Sustainable Energy Technologies

Abstract

This work focuses on how Big Data, Generative AI, and Cloud Connectors could advance the Education IT solutions and Sustainable Energy Technologies. Data Technology excellence is a process of value creation through information processing that increases producers’ and users’ welfare. Companies interested in revenue optimization from IoT-like data field data streams for big companies should resort to Big Data Dimensional Solutions for sensor network enhancement, maintenance, value extraction, and performance monitoring.
Data collection, storage, processing, and data monetization have a comprehensive technological approach and relevant business fields. Education IT solutions are concerned with applications in online learning platforms and open educational resources. Technologies currently effective in education and ICT solutions are examined. The illness stage of these technologies is proposed to be overcome. New generation technologies like Augmented Reality, Virtual Reality, and 5G are suggested to have a growing share in education. Generative AI applications are expected to gain popularity in more conservative-angle pedagogical practices and gain share as education assessment supporters.
Two-way cloud connectors extending cloud computing advantages closer to IoT edge are proposed as a means for providing administrators of multi-cloud platforms with flexible security options and vendors with interoperability indicators of their cloud services. From the functional side, the developed protocol allows metering transmissions. Public key infrastructure supports data confidentiality, authenticity, and integrity. A signature-free approach enhances security and reduces time overhead. From the performance standpoint, a dual-communication channel architecture enables quick-time transmissions with the required bandwidth and quality. Experimental validation illustrates improved privacy levels and performance gains compared to other cloud connectors. The proposed Clouds-as-a-Service provisioning framework empowers hardware-agnostic and resolvable cloud solutions’ orchestration in public cloud ecosystems without vendor locking with pay-per-use costs. It extends the state-of-the-art by applying machine learning techniques to mediation service search and selection. The impact of cloud recommendations on QoS provisioning is assessed. Experimental validation highlights improved heterogeneity levels and reduced search time overhead when using the recommender system.