A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments

TL;DR

This paper compares four population-based algorithms for workflow scheduling in cloud-fog environments, focusing on optimizing makespan, cost, and energy, with a hybrid GA-PSO showing slightly better performance.

Contribution

It introduces a weighted sum objective function for workflow scheduling and evaluates four algorithms using FogWorkflowSim, highlighting the hybrid GA-PSO's effectiveness.

Findings

Hybrid GA-PSO outperforms standard algorithms.

Weighted sum objective function effectively balances multiple objectives.

Simulation results validate the proposed evaluation framework.

Abstract

This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. This work also provides the motivational groundwork for the weighted sum objective function for the workflow scheduling problem and develops this function based on three objectives: makespan, cost and energy. The recently proposed FogWorkflowSim is used as the simulation environment with the aforementioned objectives serving performance metrics. Results show that hybrid combination of the GA-PSO algorithm exhibits slightly better than the standard algorithms. Future work will include expansion of the workflows used by increasing the number of tasks as well as adding some more workflows. The addition of some more objectives to the weighted objective function will also be pursued