Intelligent Resource Orchestration and Workload Forecasting in End-Edge-Cloud Collaborative Ecosystems: A Deep Learning and Metaheuristic Approach to Optimizing Cost, Delay, And Service Level Agreements

Abstract

The rapid proliferation of Internet of Things (IoT) devices and resource-intensive mobile applications has necessitated a paradigm shift from centralized cloud computing to a multi-tiered end-edge-cloud orchestrated architecture. Central to the efficiency of these modernized systems is the ability to predict fluctuating workloads and intelligently place tasks to satisfy stringent Service Level Agreements (SLAs) while minimizing operational costs and energy consumption. This research presents a comprehensive investigation into the integration of Artificial Neural Networks (ANN), Deep Reinforcement Learning (DRL), and metaheuristic optimization for workload characterization and task offloading. We explore the nuances of long short-term memory recurrent neural networks (LSTM-RNN) for time-series forecasting in cloud datacenters and the application of deep Q-learning for workflow scheduling in mobile edge computing. The study further evaluates multi-scenario offloading schedules for biomedical data and healthcare tasks, emphasizing priority-aware mechanisms like multilevel feedback queueing. By synthesizing evidence from large-scale utility clouds and Google compute clusters, this article establishes a robust framework for joint computation offloading and user association. The results highlight that hybridized models-combining predictive analytics with refined optimization algorithms-significantly outperform static scheduling policies in dynamic, multi-task environments. This work concludes with a deep interpretation of the trade-offs between energy efficiency and delay guarantees, providing a roadmap for future self-aware computing systems.