Energy-Efficient Task Scheduling in Fog Environment Using TOPSIS

As IoT devices proliferate quickly, the amount of data generated has increased rapidly, requiring analysis and storage in cloud data centers. To mitigate high data traffic and reduce latency, fog emerged as a paradigm that brings cloud services closer to users through accessible networks. By doing so, fog computing alleviates traffic congestion and delays. Moreover, fog devices are constrained in terms of power supply, processing capabilities, and communication resources, making it challenging to design fog systems that meet real-time application requirements. Fog devices are having limited power supply because most of them are battery operated, thus energy conservation becomes a crucial objective. Energy-efficient task scheduling is one strategy to reduce energy consumption in fog systems. However, finding an efficient scheduling strategy that balances system performance, energy consumption and meets service level agreements remains a challenge. Therefore, to address these issues, in this paper, we proposed an energy efficient scheduling algorithm using a multi-criteria approach i.e. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize energy, execution performance and cost in fog computing. To evaluate the proposed approach, we conducted simulations in MATLAB and the results demonstrate its effectiveness in significantly reducing makespan, cost and energy usage in the fog computing environment.