FogScheduler: A resource optimization framework for energy-efficient computing in fog environments

Abstract

The rapid growth of Internet of Things (IoT) devices has created a pressing demand for fog computing, offering an effective alternative to the inherent constraints imposed by traditional cloud computing. Efficient resource management in fog environments remains challenging due to device heterogeneity, dynamic workloads, and conflicting performance objectives. This paper introduces FogScheduler, an innovative resource allocation algorithm that optimizes performance and energy efficiency in IoT-fog ecosystems using the TOPSIS method to rank resources based on attributes like MIPS, Thermal Design Power (TDP), memory bandwidth, and network latency. Experiments highlight FogScheduler's notable achievements, including a 46.1 % reduction in energy consumption in the best case compared to the Greedy Algorithm (GA) and a 45.6 % reduction in makespan compared to the First-Fit Algorithm (FFA). On average, FogScheduler achieves a 27 % reduction in energy consumption compared to FFA, demonstrating its consistent ability to optimize resource allocation. Even in worst-case scenarios, FogScheduler outperforms traditional algorithms, underscoring its robustness across varying resource contention levels. Results from our experiments demonstrate that FogScheduler is a highly effective solution for energy-aware and performance-optimized resource management, offering significant potential for IoT-fog-cloud ecosystems.