Enhancing Sustainability of Online Education via a Validated Cloud Management Framework

Abstract

The rapid proliferation of large-scale online education has become an indispensable component of modern educational systems, yet it faces persistent sustainability bottlenecks including unbalanced cloud resource allocation, excessive energy consumption, low service stability, and poor long term operational scalability. Existing cloud management strategies for online learning platforms primarily focus on single-dimensional performance optimization such as latency reduction or resource load balancing, while ignoring the coupling relationship among energy efficiency, service quality, economic cost, and user learning experience, which severely restricts the sustainable development of online education ecosystems. To address the above research gaps, this paper proposes a multi dimensional validated adaptive cloud management framework (VCMF) tailored for large-scale online education scenarios. Firstly, a hierarchical cloud resource partitioning model is constructed to classify educational business resources according to learning scenario attributes. Secondly, we design a dual-objective optimization algorithm combining improved particle swarm optimization (IPSO) and greedy dynamic scheduling strategy, which realizes collaborative optimization of system energy consumption and service quality. Thirdly, a multi-index validity verification mechanism based on educational service characteristics is established to dynamically calibrate framework parameters and eliminate scheduling deviation caused by fluctuating learning user traffic.Comprehensivecomparative experiments are conducted on a self-built cloud simulation platform with real online education user traffic datasets. The experimental results demonstrate that compared with state-of-the-art baseline methods, the proposed VCMF reduces system energy consumption by 18.7%-26.3%, cuts average resource idle rate by 21.5%, decreases user request response delay by 15.2%, and improves compre hensive sustainability score by 23.8%. Meanwhile, the framework exhibits excellent robustness under extreme peak traffic scenarios. This research provides a feasible theoretical basis and engineering implementation scheme for high-efficiency, low-carbon, and sustainable operational management of modern online education platforms.