Multi-Objective Monarch Butterfly Optimization Algorithm for Efficient Workflow Scheduling in an Edge-Fog-Cloud Environment

The fast expansion of the Internet of Things (IoT) creates new computational and storage challenges for both service providers and end users. IoT devices, interconnected and cooperative within Edge computing environments, generate massive amounts of data that often require real-time processing. Fog computing, a technology created to overcome the limitations of cloud computing brings computational resources closer to the edge of the network, reducing latency and enabling real-time services. It serves as a bridge between IoT devices and the cloud, collecting and processing IoT data locally, while offloading intensive tasks to the cloud when necessary. In an Edge-Fog-Cloud environment, efficient workflow scheduling is essential to optimize available resources, minimize makespan, reduce costs and save energy. However, finding an optimal solution for these conflicting objectives is a complex challenge. This paper present a Multi-Objective Monarch Butterfly Optimization Algorithm (MO-MBO) for efficient workflow scheduling in an Edge-fog-cloud environment. MO-MBO optimizes makespan, cost, and energy consumption simultaneously using Pareto dominance. Evaluations using FogWorkflowSim show the effectiveness of MO-MBO compared to traditional Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA).