Review on Workflow Scheduling In Cloud Environment: A Comprehensive Study
Keywords:
Cloud Computing, Distributed Computing, Work Flow Scheduling, Benefit Of Clouds, Load BalancingAbstract
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.
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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
