Energy-Efficient Resource Allocation Algorithms in IoT-Cloud Ecosystems: A Sustainable Computing Perspective

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to a massive surge in data generation, demanding scalable and energy-efficient computational infrastructures. Cloud and fog computing environments have emerged as prominent enablers of this ecosystem, offering centralized and edge-level processing, respectively. However, the growing energy demands of these infrastructures pose significant environmental and operational concerns. This paper explores energy-efficient resource allocation algorithms tailored to IoT-cloud ecosystems, emphasizing dynamic scheduling techniques, green computing principles, and fog-cloud collaboration. By analyzing current trends, algorithmic approaches, and performance metrics, the study offers a comprehensive view of sustainable workload management strategies. Simulation results and comparative analyses provide insight into the trade-offs between latency, throughput, and energy consumption, enabling better decision-making for green infrastructure planning.

Keywords: Green computing, dynamic scheduling, energy consumption, fog computing, workload optimization, IoT-cloud integration, resource allocation