EOA: Energy and Temperature Aware Scheduling in Cloud-Fog Computing Environment

Energy and temperature management are critical challenges in Cloud-Fog Computing (CFC) environments due to the growing demand for high-performance computing and sustainable energy consumption. This paper introduces an Energy and Temperature aware scheduling approach using the Earthworm Optimization Algorithm (EOA) to efficiently balance energy consumption and thermal conditions while maintaining optimal system performance. The proposed EOA leverages the earthworm's foraging behavior to optimize Task Scheduling (TS) by identifying energy-efficient nodes and distributing workloads to minimize temperature hotspots. By incorporating energy consumption rates and temperature thresholds into the fitness function, the algorithm dynamically schedules tasks to reduce energy usage and prevent thermal overload in both cloud and fog layers. Experimental results demonstrate that the EOA-based scheduling significantly reduces overall energy consumption and maintains stable temperature levels compared to traditional algorithms such as HDDPGTS, EEOA, and MAO. The approach also enhances resource utilization and enhances the lifespan of hardware components by preventing overheating. This makes the proposed method highly suitable for energy-constrained and thermally sensitive CFC environments, contributing to greener and more efficient computing infrastructures.