A comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environments

Abstract

Scientific workflows are denoted by interdependent tasks and computations that are aimed at achieving some scientific objectives. The scheduling of these workflows involve the allocation of the tasks to particular computational resources, traditionally on the cloud infrastructure. This process is, however, very challenging. It is associated with high computation and communication costs because scientific workflows are data-intensive and computationally complex. In recent years, there has been overwhelming interest in using population-based optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for scientific workflow scheduling, predominantly, in the cloud environments.