An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method

Toan Phan Thanh; Loc Nguyen The; Said Elnaffar

doi:doi:10.11648/j.ajcst.20180101.14

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An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method

Toan Phan Thanh, Loc Nguyen The, Said Elnaffar

Published in American Journal of Computer Science and Technology (Volume 1, Issue 1)

Received: 27 October 2017 Accepted: 4 December 2017 Published: 2 January 2018

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Abstract

The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.

Published in	American Journal of Computer Science and Technology (Volume 1, Issue 1)
DOI	10.11648/j.ajcst.20180101.14
Page(s)	24-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Workflow Scheduling, Opposition-Based Differential Evolution, Cloud Computing, Differential Evolution

References

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[9]	S. Pandey, L. Wu1, S. M. Guru, R. Buyya1, A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, Proc. of 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pages 400-407, 2010
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APA Style

Toan Phan Thanh, Loc Nguyen The, Said Elnaffar. (2018). An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. American Journal of Computer Science and Technology, 1(1), 24-30. https://doi.org/10.11648/j.ajcst.20180101.14

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ACS Style

Toan Phan Thanh; Loc Nguyen The; Said Elnaffar. An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. Am. J. Comput. Sci. Technol. 2018, 1(1), 24-30. doi: 10.11648/j.ajcst.20180101.14

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AMA Style

Toan Phan Thanh, Loc Nguyen The, Said Elnaffar. An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. Am J Comput Sci Technol. 2018;1(1):24-30. doi: 10.11648/j.ajcst.20180101.14

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@article{10.11648/j.ajcst.20180101.14,
  author = {Toan Phan Thanh and Loc Nguyen The and Said Elnaffar},
  title = {An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {24-30},
  doi = {10.11648/j.ajcst.20180101.14},
  url = {https://doi.org/10.11648/j.ajcst.20180101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.14},
  abstract = {The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.},
 year = {2018}
}

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TY - JOUR
T1 - An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method
AU - Toan Phan Thanh
AU - Loc Nguyen The
AU - Said Elnaffar
Y1 - 2018/01/02
PY - 2018
N1 - https://doi.org/10.11648/j.ajcst.20180101.14
DO - 10.11648/j.ajcst.20180101.14
T2 - American Journal of Computer Science and Technology
JF - American Journal of Computer Science and Technology
JO - American Journal of Computer Science and Technology
SP - 24
EP - 30
PB - Science Publishing Group
SN - 2640-012X
UR - https://doi.org/10.11648/j.ajcst.20180101.14
AB - The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.
VL - 1
IS - 1
ER -

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Author Information

Toan Phan Thanh

Faculty of Technology Education, Hanoi National University of Education, Ha Noi, Viet Nam
Loc Nguyen The

Faculty of Information Technology, Hanoi National University of Education, Ha Noi, Viet Nam
Said Elnaffar

School of Engineering, Computer Science Department, American University of RAK, Ras al Khaimah, UAE

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APA Style

Toan Phan Thanh, Loc Nguyen The, Said Elnaffar. (2018). An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. American Journal of Computer Science and Technology, 1(1), 24-30. https://doi.org/10.11648/j.ajcst.20180101.14

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ACS Style

Toan Phan Thanh; Loc Nguyen The; Said Elnaffar. An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. Am. J. Comput. Sci. Technol. 2018, 1(1), 24-30. doi: 10.11648/j.ajcst.20180101.14

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AMA Style

Toan Phan Thanh, Loc Nguyen The, Said Elnaffar. An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method. Am J Comput Sci Technol. 2018;1(1):24-30. doi: 10.11648/j.ajcst.20180101.14

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@article{10.11648/j.ajcst.20180101.14,
  author = {Toan Phan Thanh and Loc Nguyen The and Said Elnaffar},
  title = {An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {24-30},
  doi = {10.11648/j.ajcst.20180101.14},
  url = {https://doi.org/10.11648/j.ajcst.20180101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.14},
  abstract = {The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.},
 year = {2018}
}

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