Metaheuristic Coevolution Workflow Scheduling in Cloud Environment

Denis Nasonov, Mikhail Melnik, Natalya Shindyapina, Nikolay Butakov

2015

Abstract

Today technological progress makes scientific community to challenge more and more complex issues related to computational organization in distributed heterogeneous environments, which usually include cloud computing systems, grids, clusters, PCs and even mobile phones. In such environments, traditionally, one of the most frequently used mechanisms of computational organization is the Workflow approach. Taking into account new technological advantages, such as resources virtualization, we propose new coevolution approaches for workflow scheduling problem. The approach is based on metaheuristic coevolution that evolves several diverse populations that influence each other with final positive effect. Besides traditional population, that optimizes tasks execution order and task's map to the computational resources, additional populations are used to change computational environment to gain more efficient optimization. As a result, proposed scheduling algorithm optimizes both computation tasks to computation environment and computation environment to computation tasks, making final execution process more efficient than traditional approaches can provide.

Paper Citation

in Harvard Style

Nasonov D., Melnik M., Shindyapina N. and Butakov N. (2015). Metaheuristic Coevolution Workflow Scheduling in Cloud Environment . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 252-260. DOI: 10.5220/0005599402520260

in Bibtex Style

@conference{ecta15,
author={Denis Nasonov and Mikhail Melnik and Natalya Shindyapina and Nikolay Butakov},
title={Metaheuristic Coevolution Workflow Scheduling in Cloud Environment},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={252-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005599402520260},
isbn={978-989-758-157-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Metaheuristic Coevolution Workflow Scheduling in Cloud Environment
SN - 978-989-758-157-1
AU - Nasonov D.
AU - Melnik M.
AU - Shindyapina N.
AU - Butakov N.
PY - 2015
SP - 252
EP - 260
DO - 10.5220/0005599402520260