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Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment

A. Aggarwal1 , P. Dimri2 , A. Agarwal3

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 565-570, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.565570

Online published on Jun 30, 2019

Copyright © A. Aggarwal, P. Dimri, A. Agarwal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. Aggarwal, P. Dimri, A. Agarwal, “Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.565-570, 2019.

MLA Style Citation: A. Aggarwal, P. Dimri, A. Agarwal "Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment." International Journal of Computer Sciences and Engineering 7.6 (2019): 565-570.

APA Style Citation: A. Aggarwal, P. Dimri, A. Agarwal, (2019). Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment. International Journal of Computer Sciences and Engineering, 7(6), 565-570.

BibTex Style Citation:
@article{Aggarwal_2019,
author = {A. Aggarwal, P. Dimri, A. Agarwal},
title = {Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {565-570},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4592},
doi = {https://doi.org/10.26438/ijcse/v7i6.565570}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.565570}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4592
TI - Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment
T2 - International Journal of Computer Sciences and Engineering
AU - A. Aggarwal, P. Dimri, A. Agarwal
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 565-570
IS - 6
VL - 7
SN - 2347-2693
ER -

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Abstract

Cloud computing has been the buzzword of the ICT industry lately and has been widely accepted because of its pay-as-you-go pricing model and ease of use. Hence the bigger scientific applications which are generally represented as workflow or DAG are also moving on the cloud. Scheduling workflows on the cloud was becoming a problem as the existing traditional workflow scheduling algorithms (for Grid) were not perfectly suitable for cloud environment because of its dynamic nature. This paper tries to explore the existing algorithms for scheduling multiple workflows in cloud computing environment and presents a comparative analysis in tabular form of some existing algorithms along with their parameters, methods and tools used.

Key-Words / Index Term

Scheduling, Cloud Computing, Multiple Workflow

References

[1] P. Mell and T. Grance, “The NIST definition of Cloud Computing”, NIST, Gaithersburg, MD, USA, Tech. Rep. 6,2009.
[2] H. Topcuoglu, S. Hariri, and M.Y. Wu, “Performance-effective and low-complexity task scheduling for heterogeneous computing”, IEEE Transaction on Parallel and Distributed Systems, vol. 13, no. 3, pp. 260-274, March 2002.
[3] R. Sakellariou, H. Zhao, E. Tsiakkouri, and M.D. Dikaiakos, “Scheduling workflows with budget constraints”, in Proc. Integr. Res. Grid Computing, 2007, pp.. 189-202
[4] W. Zheng and R. Sakellariou, “Budget-deadline constrained workflow planning for admission control”, Journal of Grid Computing, vol. 11, no. 4, pp. 633-651, 2013.
[5] H. Arabnejad and J. G. Barbosa, “A budget constrained scheduling algorithm for workflow applications”, Journal of Grid Computing, vol. 12, pp. 665-679, 2014.
[6] S. Su, J. Li, Q. Huang, X. Huang, K. Shuang, and J. Wang, “Cost-efficient task scheduling for executing large problems in the cloud”, Parallel Computing, vol. 39, no. 4, pp. 177-188, 2013.
[7] R. Garg, and A.K. Singh, “Multi-objective workflow grid scheduling based on discrete particle swarm optimization”, in Proc. Swarm, Evol., Memetic computing, 2011, pp. 183-190.
[8] R. Garg, and A.K. Singh, “Multi-objective workflow grid scheduling using ε-Fuzzy dominance sort based discrete particle swarm optimization”, Journal of Supercomputing, vol. 68, no. 2, pp. 709-732, 2014.
[9] Z. Zhu, G. Zhang, M. Li, and X. Liu, “Evolutionary Multi-objective Workflow Scheduling in Cloud”, IEEE Transactions on Parallal and Distributed Systems, vol. 27, no. 5, pp. 1344-1356, 2016.
[10] T.A. L. Genez, L.F.Bittencourt, E. R. M. Madeira, “Workflow scheduling for SaaS/PaaS cloud providers considering two SLA levels”, IEEE Network Operations and Management Symposium, 2012.
[11] S. Sharif, J. Taheri, A. Y. Zomaya, “Online multiple workflow scheduling under Privacy and Deadline in Hybrid Cloud Environment”, 6th International Conference on Cloud Computing Technology and Science, IEEE Computer Society, 2014
[12] Y. Wang, C. Jia, Y. Xu, “Multiple DAGs Dynamic Workflow Schedduling based on the Primary Backup Algorithm in Cloud Computing System”, 9th International Conference on Broadband and Wireless Computing, Communication and Applications, IEEE, 2014
[13] T. Thanavanich, A. Siri, K. Boonlom, and A. Chaikaew, “Energy-aware Scheduling of Multiple Workflows Application on Distributed Systems”, 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2016
[14] S. Liu, K.Ren, K. Deng, and J. Song, “Time dependence based scheduling strategy for multiple workflows on IaaS cloud platform”, International Symposium on Computer, Consumer and Control, IEEE Computer Society, 2016.
[15] R. Duan, R, Prodan, and X. Li, “Multi-Objective Game Theoretic Scheduling of Bag-of-Tasks Workflows on Hybrid Clouds”, IEEE Transactions on Cloud Computing, vol. 2, no. 1, 2014.
[16] M. A. Rodriguez, and R. Buyya, “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds”, IEEE Transactions on Cloud Computing, 2014
[17] H. M. Fard, R. Prodan, and T.Fahringer, “A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud Environments”, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1203-1212, 2013.
[18] B. Lin, W. Guo, N. Xiong, G, Chen, A. V. Vasilakos, and H.Zhang, “A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments”, IEEE Transactions on Network and Service Management, vol. 13, no. 3, pp. 581-594, 2016.
[19] B.P. Rimal , and M. Maier, “Workflow Scheduling in Multi-Tenant Cloud Computing Environments”, IEEE Transactions on Parallel and Distributed Systems, 2015.
[20] X. Li, L. Quian, and R. Ruiz, “Cloud Workflow Scheduling with Deadlines and Time Slot Availability”, IEEE Transactions on Parallel and Distributed Systems, 2015.
[21] K. Deng, K. Ren, M. Zhu, and J. Song, “A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows”, IEEE transactions on Cloud Computing, Dec. 2015.
[22] Z. Li, J. Ge, H. Hu, W. Song, and B. Luo, “Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in clouds”, IEEE Transactions on Services Computing, 2015.
[23] M. A. Rodriguez, and R. Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithms for Scientific Workflows on Clouds”,, IEEE transactions on Cloud Computing, Vol. 2. No. 2, April-June 2014.