Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing

The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the order in which offloaded tasks are executed affects the system’s efficiency. For instance, executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system. Therefore, an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy. In this paper, we introduced a multi-objective and enhanced version of Cheeta Optimizer (CO), namely (MoECO), to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives, i.e., energy consumption and communication delay. MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority. The mathematical modelling of CO needs improvement in computation time and convergence speed. Therefore, MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location. The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase, i.e., global search strategy. Consequently, this prevents the algorithm from getting trapped in the local optimal solution. Moreover, the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah. This increases the exploitation capability of agents, i.e., local search capability. Furthermore, MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives. Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.