Energy-Efficient Load Balancing in Fog Computing Using a Hybrid Dijkstra Greedy Approach

Abstract

Fog computing has become a key paradigm for latency-sensitive Internet of Things (IoT) applications by bringing computation closer to data sources. It extends processing capabilities to the network edge, thereby reducing reliance on centralized cloud infrastructures and enabling realtime data handling. However, the distributed and heterogeneous characteristics of fog environments create significant challenges in resource management and energy efficiency, especially when distributing workloads across dynamically changing nodes. To overcome these challenges, this paper aims to distribute workloads efficiently among fog nodes. It introduces a Hybrid Energy-Aware Load Balancing (EALB) algorithm that integrates Dijkstra’s shortest path algorithm for routing with a greedy resource allocation strategy. To evaluate the proposed algorithm, simulations were conducted across multiple deployment scales, from small IoT setups to large smart city environments. Results demonstrate that, unlike existing approaches, the proposed method simultaneously accounts for network topology and resource availability when making load balancing decisions. The results indicate notable improvements in average latency (5.36% in medium-scale and 4.55% in large-scale deployments), together with a manageable trade-off in power consumption. By combining shortest-path routing with greedy allocation, the hybrid model improves latency performance as the system scales, besides a slight increase in energy usage. In small-scale scenarios, the Greedy algorithm achieves superior performance in both latency (13.4% reduction in total latency) and energy consumption (5.57% lower power use). However, in large-scale environments, the Hybrid EALB algorithm reaches substantial latency gains with only a marginal increase in power consumption (0.36%). Overall, the EALB approach enables more intelligent routing and task allocation through its integrated strategy, effectively balancing latency and energy efficiency. These results show how useful hybrid optimization approaches can be when building sustainable fog computing architectures. They also offer practical guidance on choosing the right load balancing strategy, depending on the system’s size, network structure, and specific performance needs.