Smartflux: A Dual-Phase Resource Orchestration Model for IOT-Fog-Cloud Ecosystems

Abstract - The rapid expansion of Internet of Things (IoT) ecosystems has triggered an immense surge in data generation, demanding computational models that can efficiently manage and process this influx. Although cloud computing provides scalable resources for such tasks, its inherent latency and lack of contextual responsiveness limit its effectiveness for time-sensitive IoT applications. Fog computing, introduced to bridge this gap by enabling localized processing closer to data sources, offers reduced latency but is constrained by limited computational capacity. To overcome these limitations, this research introduces a hybrid IoT–fog–cloud framework that strategically balances real-time responsiveness with computational scalability. A two-phase resource allocation mechanism is proposed: initially, tasks are distributed based on a task guarantee ratio to either fog or cloud layers; subsequently, a Bayesian classifier refines this allocation using historical data for adaptive scheduling. To further enhance performance, the Crayfish Optimization Algorithm (COA), a novel bio-inspired metaheuristic, is employed to minimize execution delays and system latency. Simulations conducted via the iFogSim toolkit confirm the effectiveness of the proposed model, showcasing superior task handling and reduced latency compared to existing approaches. Keywords: IoT, fog computing, cloud computing, resource allocation, task classification, Bayes classifier, COA.