Optimizing Job Scheduling for Improved Efficiency in Fog Computing

Abstract

Energy efficiency is a critical concern in fog computing systems, particularly in multi-node frameworks where effective resource allocation and workload distribution are vital. This study investigates the application of dynamic resource allocation (DRA) and data compression techniques to enhance the overall efficiency and energy consumption of a 4-node fog computing system. By dynamically distributing workloads based on node utilization and available resources, the DRA approach optimizes resource utilization and minimizes energy consumption. Additionally, the incorporation of data compression techniques reduces network traffic and communication overhead, further contributing to energy savings. Through comprehensive analysis and estimation of energy usage across multiple nodes, this study quantifies the energy costs associated with processing, compression, and aggregation operations. The findings highlight the potential of DRA and data compression techniques in minimizing energy consumption and enhancing the overall efficiency of fog computing systems. By intelligently managing resources and reducing data transmission overhead, the proposed approach demonstrates significant improvements in energy efficiency without compromising the performance of the fog computing infrastructure. The study provides valuable insights into the optimization of job scheduling and resource management strategies for fog computing environments. The results can inform the design and implementation of energy-efficient fog computing architectures, paving the way for more sustainable and cost-effective deployment of fog-based applications and services.