JoWUA

Abstract

The increasing popularity of cloud computing e-learning platforms presents several difficulties in managing the computation and networking resources effectively based on the changing requirements and demands of a large number of learners. Conventional methods have often shown high delays, improper bandwidth usage, lack of scalability, and low QoS in such highly dynamic and diverse environments, especially for wireless and heterogeneous cases. This research article focuses on the implementation of the CRL-RA approach for achieving better performance optimization in the cloud-based e-learning platform. In particular, the Cross-Layer Resource Allocation algorithm involves the reinforcement learning process for the analysis of network status, user workload, server utilization, bandwidth, and delay. Through interactions within the cloud platform, the algorithm is able to find effective allocation rules. In addition, cross-layer monitoring and adaptive workload balancing are used to facilitate the provision of highly scalable education services in cloud infrastructure. Experimental results confirmed the superiority of the new approach to resource allocation over traditional methods. Throughput was measured at 96.8%, resource utilization efficiency at 94.5%, quality of service accuracy at 97.1%, and latency at 89 ms, while energy consumption was minimized. The analysis of the effectiveness of the use of reinforcement learning optimization and cross-layer collaboration further demonstrated the importance of both approaches to improve the adaptability and service delivery of such systems. Thus, the proposed framework proves to be very efficient in the development of cloud-based e-learning systems in dynamically changing environments. The proposed solution represents a smart way to reduce power consumption in e-learning infrastructures of the next generation. Moreover, the framework shows promising potential for the development of further applications using edge computing and deep reinforcement learning.