Distributed Adaptive Learning Pipelines for Dynamic Online Education Systems
Keywords:
Pipeline automation, Reinforcement learning–based orchestration, Dynamic online education systems, Resource-aware task scheduling, Latency and throughput optimization, Learning-driven system adaptation.Abstract
Scalable and flexible learning infrastructure that is operational when under high dynamism workloads has been accentuated by the fast development of online education platforms. Conventional learning pipelines are based on either an inertial or heuristically based orchestration, which are not able to support performance stability in distributed settings with a changing learner demand. The framework of distributed adaptive learning pipelines to online education systems that can be dynamically owned is proposed in this paper, in which the resources distribution and pipeline coordination is dictated by the adaptive mechanisms of learning processes. The suggested framework treats the pipeline control as a sequential decision-making process and has utilised a reinforcement learning-driven orchestration approach to dynamically reconfigure pipeline components at remote nodes. The framework then adjusts scheduled tasks and the use of resources by constantly monitoring system states and performance feedback to minimize end to end latency with increased throughput and scalability. Extensive simulation based assessments are carried out at different loads of learners and system characteristics. Findings show that the proposed adaptive pipeline is significantly better than both the static and heuristic baseline in the metrics of the reduction of latency, throughput, scalability, and orchestration stability, which does not reduce the strong adaptive overhead. The results indicate that learning-driven pipeline automation has high efficiency in attaining sound and successful operation of the distributed online educations and can support next-generation learning platforms with scalability and intelligence.
