A GA based approach for task scheduling in multi-cloud environment

TL;DR

This paper introduces a genetic algorithm approach to optimize task scheduling in multi-cloud environments, aiming to minimize makespan despite resource heterogeneity and NP-Complete complexity.

Contribution

It presents a novel GA-based method with an innovative fitness function and mutation strategy specifically designed for multi-cloud task scheduling.

Findings

The proposed algorithm reduces makespan significantly compared to baseline methods.

Performance tested on various benchmark datasets shows robustness and efficiency.

The approach effectively handles heterogeneity in cloud resources.

Abstract

In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datasets and its performance is evaluated in terms of total makespan.