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Jyoti Mishra
Independent Researcher
Uttar Pradesh, India
Abstract
Edge computing has rapidly evolved as a transformative paradigm in educational technology (EdTech), offering a decentralized approach that brings data processing and intelligence closer to end users. In this comprehensive study, we investigate how edge computing architectures can fundamentally enhance the scalability, performance, and cost-effectiveness of modern EdTech platforms. Leveraging a mixed-methods design—comprising both quantitative benchmarking of prototype services and qualitative surveys of 150 EdTech administrators across three continents—we assess key metrics such as latency, throughput, bandwidth utilization, node utilization, and stakeholder satisfaction. Our experimental deployments contrast a traditional cloud-only model with a hybrid edge–cloud framework that distributes tasks across centralized data centers and three strategically placed micro data centers in New Delhi, Bengaluru, and Kolkata. Results reveal that edge-enabled solutions achieve up to a 60% reduction in average request latency, a 133% increase in throughput per region, and a 45% decrease in bandwidth consumption, all while maintaining robust CPU and memory utilization. Survey feedback underscores high administrator confidence in performance gains and cost savings, balanced against moderate concerns over deployment complexity and maintenance overhead. Educationally, edge computing unlocks near-instantaneous interactive experiences—such as live quizzes, AR/VR tutoring, and adaptive content delivery—facilitating richer learner engagement and real-time feedback loops. Moreover, localized data processing enhances privacy compliance and bridges connectivity gaps in under-resourced regions by caching popular content at the network edge. This manuscript culminates in actionable guidelines for practitioners, including pilot deployment strategies, training imperatives for IT staff, and recommendations for vendor collaboration. By integrating edge computing into EdTech ecosystems, institutions can build resilient, scalable learning environments that adapt to evolving pedagogical demands, support equitable access, and pave the way for future innovations in distributed AI and automated edge orchestration.
Keywords
Edge computing; scalability; educational technology; low latency; hybrid architectures
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