A Meta-Heuristics-Based Solution for Multi-Objective Workflow Scheduling in Fog Computing

In recent years, there has been a significant increase in the volume of data generated by Internet of Things (IoT) applications, mostly driven by the rapid proliferation of IoT devices and advancements in communication technologies. The conventional cloud computing network was not specifically built to handle such a vast volume of data, leading to several issues, including increased processing time, higher costs, larger band-width usage, increased power usage, and communication delays. As a solution, conventional cloud servers have been expanded to additional layers of computing, storage, and network, termed as cloud-fog computing. The cloud-fog computing provides storage, processing, networking, and analytics capabilities in close proximity to IoT devices. The problem of scheduling work-flow applications in cloud-fog environments to optimize several conflicting objectives is classified as computationally complex. Particle Swarm Optimization is the widely recognized evolutionary meta-heuristic and is the optimal method for implementing multi-objective solutions because of its user-friendly approach and quick converging capability. Despite its wide acceptance, it does have several drawbacks, such as early convergence and solution stagnation. In order to overcome these limitations, this paper establishes a comprehensive theoretical model to schedule workflow applications for cloud-fog systems. The proposed model employs various competing objectives, such as power usage, overall cost, and makespan. To achieve this, we introduce a novel algorithm, learning enhanced particle swarm optimization (LE-PSO), which incorporates an inverse tangent inertia weight policy and adaptive learning factor methods. The efficiency of the LE-PSO is subsequently assessed by employing an operational data set of scientific workflow applications within a cloudsim-based simulation and validated against GAMPSO, EMMOO, PSO, and GA state-of-the-art approaches. The workflow scheduling, we suggest achieves the substantial decrease in makespan and power usage while maintaining the total cost at an optimal level, in comparison to existing meta-heuristics.