## 2 Related Work  
Efficient scheduling is critical to improving a system's performance. As a result, substantial research on fog-cloud computing job scheduling has been performed to address the challenge of distributing work to a collection of computers. Various approaches for task scheduling in fog-cloud computing have been developed, and a plethora of algorithms have been offered in the past to meet the issues connected to this environment. The following are some of these algorithms. In ref. [11], in a cloud-fog scenario, the author describes a new technique for scheduling IoT device requests. This method involves incorporating Slap Swarm Algorithm (SSA) operators into an existing optimization methodology known as artificial ecosystem-based optimization (AEO). This move is intended to strengthen AEO's ability to address IoT-related concerns. The author also introduces ATS-FOA, a revolutionary collection of techniques that integrate algorithms such as approximate nearest neighbor (ANN) and fruit fly optimization (FOA) to reduce processing time, latency, memory utilization, and cost. In ref. [12], the authors propose a new technique for scheduling tasks, which enhances firework algorithms. They suggest introducing a detection system for the explosion radius of fireworks. This approach can be employed in a cloud computing and fog computing system to reduce task processing time and achieve more effective load balancing across fog devices. In ref. [13] researchers propose exploring serverless computing and its advantages for event-driven cloud applications. The proposed machine learning model utilizes gray wolf optimization (GWO) and reinforcement learning (RIL) to parallelize tasks and optimize task allocation in the serverless framework. The results of the simulation experiments show that the GWO-RIL approach reduces runtimes, adapts to varying load conditions, and achieves energy savings while maintaining similar latency. The findings suggest the potential for further development and research in distributed deep learning, task allocation for cloud-based and edge-based applications, and task offloading. In ref. [6], the authors propose a new heuristic algorithm depending on the deadline and task bandwidth using the ACO algorithm for IoT task scheduling in a fog-cloud environment, optimizing Makespan and task deadline satisfaction. It prioritizes the earliest deadlines, assigning tasks to resources for minimum completion time. The evaluation shows superiority over existing methods in achieving low Makespan and high deadline satisfaction. In ref. [14], the authors propose a new approach for the effective scheduling and activation management of fog computing services using the multi-objective gray wolf optimization (GWO) technique. This approach employs a multi-objective function to allocate resources and handle tasks, aiming to strike a balance between energy consumption and task execution time. The proposed method comprises two main stages: first, the GWO algorithm is utilized for scheduling, and then, container migration is employed for task offloading and resource allocation. In ref. [15], researchers propose aims to address the problem of task scheduling in cloud computing environments, which is crucial for improving performance and scheduling tasks on virtual machines. The authors propose a modified gray wolf optimization (MGWO) algorithm, which utilizes a multi-objective fitness function that considers both Makespan and cost. By reducing both the cost and Makespan, the proposed technique aims to increase job scheduling efficiency. In their work [16], the authors propose using Bumble Bee Mating Optimization (BBMO); the authors present a practical work scheduling approach for cloud computing. The strategy seeks to reduce Makespan when running tasks across several virtual machines (VMs). When compared to Honey Bee Mating Optimization (HBMO) and the Genetic Algorithm (GA), BBMO surpasses both. However, the additional complexity of BBMO necessitates more calculation time.