Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing

The cloud-fog computing paradigm is a novel hybrid computing model that delivers computational services to Fog nodes situated near data sources. This paradigm features a volatile and dynamic network topology, comprising heterogeneous IoT devices with varying computational capabilities, alongside a large number of diverse end-user requests. These complexities present significant challenges for researchers in establishing a robust, energy-efficient, and reliable communication environment. Efficient and optimal task scheduling is among these challenges, as it involves finding appropriate computing resources for processing tasks. Assigning tasks to fog nodes reduces delay but increases energy consumption, while routing tasks to cloud servers conserves energy but prolongs transmission delay. Therefore, it is essential to develop an optimal task scheduling algorithm for a reliable, delay-efficient, and energy-efficient communication environment. To address this, we propose a multiobjective Harris hawks optimization (HHO)-based task scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task scheduling with the objectives of minimizing delay and energy consumption. MoHHOTS is implemented in MATLAB and evaluated against state-of-the-art benchmark algorithms, including MOGWO and the cloud-fog cooperation algorithm. Leveraging the high convergence and stochastic operators of the HHO algorithm, alongside a balanced approach to iteration between diversification and intensification, the proposed algorithm provides a set of tradeoff solutions via the Pareto-optimal Front. Simulation results demonstrate the efficacy of the proposed solution, achieving improvements of up to 25% over a similar scheduling algorithm in terms of optimizing transmission delay and energy consumption.