Evaluating the Impact of ICT Innovations on Virtual Machine Learning Efficiency in Cloud Computing Environments
Keywords:
Cloud Computing, Virtual Machines, Machine Learning Efficiency, ICT Innovations, Resource Optimization, Virtualization, Edge Computing, Performance Evaluation, Energy Efficiency.Abstract
The rapid advancement of Information and Communication Technology (ICT) has significantly transformed cloud computing environments, particularly in enhancing the efficiency of virtual machine (VM)-based machine learning (ML) systems. This paper evaluates the impact of ICT innovations—such as virtualization optimization, edge-cloud integration, high-speed networking, and intelligent resource management—on the performance and efficiency of ML workloads deployed on virtual machines. The study focuses on key performance indicators including computational latency, resource utilization, energy consumption, and model training time. A comparative analysis is conducted using traditional VM configurations and ICT-enhanced cloud infrastructures to assess improvements in scalability, responsiveness, and cost efficiency. Experimental results demonstrate that the integration of advanced ICT techniques significantly reduces execution time, optimizes resource allocation, and improves overall system throughput. Furthermore, the adoption of adaptive scheduling and automated resource provisioning enhances the reliability and performance consistency of ML tasks in dynamic cloud environments. The findings highlight the critical role of ICT innovations in enabling efficient, scalable, and sustainable machine learning operations within virtualized cloud systems.
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