AI for Predictive Load Forecasting in Edge-to-Cloud Architectures: A Multi-Tier Resource Optimization Framework
Keywords:
Artificial Intelligence, Predictive Load Forecasting, Edge-to-Cloud ArchitectureAbstract
The rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time processing have led to a paradigm shift towards edge-to-cloud computing architectures. These architectures leverage computational resources at both the edge and the cloud to deliver efficient and scalable solutions. Predictive load forecasting, which anticipates future computational demand, is a critical component in optimizing resource allocation across edge and cloud layers. This paper proposes a multi-tier AI-based resource optimization framework for predictive load forecasting in edge-to-cloud systems. By utilizing machine learning (ML) and deep learning (DL) models, the framework predicts load patterns, enabling dynamic resource allocation, load balancing, and improved overall system efficiency. The paper explores key techniques for load forecasting, challenges in implementing AI models in distributed environments, and the potential benefits of AI-driven predictive systems in edge-to-cloud architectures. The proposed framework is demonstrated with case studies in smart cities and industrial IoT systems.
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