Review on Workflow Scheduling In Cloud Environment: A Comprehensive Study

Authors

  • Pratiksha PG Scholar, Department of Computer Science & Engineering, Integral University, Lucknow, India Author
  • Afsaruddin Assistant Professor, Department of Computer Science & Engineering, Integral University, Lucknow, India Author

Keywords:

Cloud Computing, Distributed Computing, Work Flow Scheduling, Benefit Of Clouds, Load Balancing

Abstract

For enterprise application development,  cloud computing is going to new-fangled paradigm that  meets the needs of today's enterprises and helps them  efficiently executing workflows in business and scientific  applications. Workflow applications often require a very  intricate runtime environment that is hard to create and  maintain. Thus, cloud infrastructure is valued as an  implementation platform for positioning technical  workflows with features such as on-demand provisioning,  scalability, reproducibility and resilience. To schedule  multiple workflows in the cloud, we need to design the  workflow before scheduling it. Therefore, in this paper we  review the diverse work done by researchers for the  workflow design as well as workflow scheduling.

Downloads

Download data is not yet available.

References

S. Srivastava, M. Haroon, and A. Bajaj, “Web document information extraction using class attribute approach,” Proc. - 4th IEEE Int. Conf. Comput. Commun. Technol. ICCCT 2013, pp. 17–22, 2013, doi: 10.1109/ICCCT.2013.6749596.

H. S. Kharkwal, “Automated Task Allotment in Unmanned Submarines by Smart Searching Algorithm,” vol. 13, no. 2, 2017.

R. Khan, M. Haroon, and M. S. Husain, “Different technique of load balancing in distributed system: A review paper,”

Glob. Conf. Commun. Technol. GCCT 2015, no. Gcct, pp. 371–375, 2015, doi: 10.1109/GCCT.2015.7342686 [4] X. Zhou, G. Zhang, J. Sun, J. Zhou, T. Wei, and S. Hu, “Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT,” Futur. Gener. Comput. Syst., vol. 93, pp. 278–289, 2019, doi: 10.1016/j.future.2018.10.046.

M. S. Husain and D. M. Haroon, “an Enriched Information Security Framework From Various Attacks in the Iot,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 8, no. 4, 2020, doi: 10.21276/ijircst.2020.8.4.3.

P. Wang, Y. Lei, P. R. Agbedanu, and Z. Zhang, “Makespan Driven Workflow Scheduling in Clouds Using Immune Based PSO Algorithm,” IEEE Access, vol. 8, pp. 29281– 29290, 2020, doi: 10.1109/ACCESS.2020.2972963.

Zhang, G., Sun, J., Zhou, J., Wei, T. and Hu, S., “Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort-based HEFT”, Future Generation Computer Systems, 93, pages.278-289,2019

Piotr Chrz, Astowski-Wachtel, Boualem Benatallah, Rachid Hamadi, Milton ODell, and Adi Susanto. “ A top-down petri net-based approach for dynamic workow modelling” LNCS, 2678:336-353, 2003.

Irene Vanderfeesten, Hajo A. Reijers, Wil M.P., and van der Aalst, “Product based workow support: A recommendation service for dynamic workow execution” LNCS, 5074:571- 574, 2008.

Dr. Mark Klein and Prof. Chrysanthos Dellarocas, “A knowledge-based approach to handling exceptions in workow systems” Journal of Computer Supported Collaborative Work, Special Issue on Adaptive Workow Systems, pages 1- 11, 2000.

Lin Chen, Minglu Li Jian, and Cao YiWang, “An eca rule based workflow design tool for shanghai grid”, Services Computing (SCC05)., 2005.

Macro Comuzzi and Irene T.P. Vanderfeesten, “Product based workflow design for monitoring of collaborating buisness processes” CAiSE, Bibliography 132, pages 154- 168, 2011.

Therani Madhusudan, “An agent-based approach for coordinating product design workflows”. International journal of science direct Computers in Industry, pages 235- 239, 2005.

P.Varalakshmi, Aravindh Ramaswamy, Aswath Balasubramanian, and Palaniappan Vijaykumar, “An optimal workow based scheduling and resource allocation in cloud”, International Conference, pages 411-420, 2011

Xiaofeng Wang, Chee Shin Ye, Rajkumar Buyya, and Jinshu Su, “Optimizing the makespan and reliability for workow applications with reputation and a look-ahead genetic algorithm” Future Generation Computer Systems, (27): 11241134, 2011.

Saeed Parsa and Reza Entezari-Maleki, “Rasa: A new task scheduling algorithm in grid environment”, World Applied Sciences Journal 7 Special Issue of Computer, IT, pages 152- 160, 2009.

Piotr Chrz, Astowski-Wachtel, Boualem Benatallah, Rachid Hamadi, Milton ODell, and Adi Susanto. “ A top down petri net-based approach for dynamic workow modelling” LNCS, 2678:336-353, 2003.

Meng Xu, Lizhen Cui, Haiyang Wang, and Yanbing Bi, “A multiple qos constrained scheduling strategy of multiple workflows for cloud computing”, IEEE International Symposium on Parallel and Distributed Processing, 2009., pages 629-634, 2009.

Cui Lin and Shiyong Lu, “Scheduling scientific workflows elastically for cloud computing”, IEEE 4th International Conference on Cloud Computing, 2011.

Maria Alejandra Rodriguez and Rajkumar Buyya, “Deadline based resource provisioning and scheduling algorithm for

scientific workflows on clouds” IEEE Transactions On Cloud Computing, (2):222-235, May 2014

Kumar, M., Sharma, S.C., Goel, A. and Singh, S.P., “A comprehensive survey for scheduling techniques in cloud computing”. Journal of Network and Computer Applications, 143, pp.1-33, 2019

Downloads

Published

2021-03-30

How to Cite

Review on Workflow Scheduling In Cloud Environment: A Comprehensive Study . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(2), 84–88. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11570