Serverless Computing Optimization for Event-Driven Applications in Education Sector

This work explores optimization strategies for serverless computing in educational, event-driven applications such as online learning platforms, real-time assessments, interactive classroom tools, and automated feedback systems. These applications require high responsiveness, low latency, and dynamic scalability—characteristics that traditional server-based infrastructures often struggle to deliver due to issues like inefficient resource utilization, over-provisioning, and performance bottlenecks. The study proposes an enhanced serverless framework that incorporates dynamic resource allocation, intelligent scheduling, predictive workload management, and efficient function orchestration to reduce cold-start delays and improve overall throughput. The framework is evaluated across multiple deployment scenarios, demonstrating significant improvements in execution time, scalability, cost-effectiveness, availability, and fault tolerance. By enabling real-time educational applications to perform reliably under fluctuating workloads, this optimized serverless approach supports a more scalable and resilient digital learning ecosystem. The findings contribute to ongoing efforts in cloud-based educational technology and provide a foundation for future innovations in serverless system design.