Energy-efficient Cloud Infrastructure for IoT Device Management: A Comprehensive Analysis of Edge-Cloud Workload Distribution Strategies

Abstract

The rapid expansion of the Internet of Things (IoT) has led to significant increases in data traffic and computational demands on cloud infrastructures, raising concerns about energy consumption in data centers. This article explores innovative approaches to creating energy-efficient cloud infrastructures for managing IoT device fleets through optimized workload distribution between edge devices and cloud resources. It proposes implementing energy-aware algorithms that dynamically determine the optimal location for data processing based on energy consumption, latency requirements, and workload characteristics. The system employs advanced machine learning techniques for workload prediction and resource allocation, demonstrating substantial improvements in energy efficiency while maintaining high-performance standards. Case studies in smart cities, agricultural monitoring, and transportation networks validate the effectiveness of this approach in real-world scenarios. The results indicate that intelligent workload distribution across edge and cloud platforms can significantly reduce energy consumption while enhancing system performance and operational efficiency, providing a sustainable pathway for large-scale IoT deployments.