1.
S.M.Jaybhaye and Vahida Z. Attar, “A Review on Scientific Workflow Scheduling in Cloud Computing”, Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number: CFP17AWO-ART, ISBN: 978-1-5090-5013-0
2.
Bhaskar Prasad Rimal, Martin Maier, “Workflow Scheduling in Multi- Tenant Cloud Computing Environments”, IEEE Transactions on parallel and distributed systems , Vol 28, No. 1, Jan 2017, pp 290-304.
3.
G. Natesan and A. Chokkalingam, “Multi-Objective Task Scheduling Using Hybrid Whale Genetic Optimization Algorithm in Heterogeneous Computing Environment,” Wireless Personal Communications, 2019.
4.
G. Juve and E. Deelman, “Scientific Workflows in the Cloud,” Grids, Clouds and Virtualization, pp. 71–91, 2011
5.
S. Saeedi, R. Khorsand, S. Ghandi Bidgoli, and M. Ramezanpour, “Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing,” Computers and Industrial Engineering, vol. 147, no,June, p. 106649, 2020
6.
Juve, Gideon, Ann Chervenak , EwaDeelman, ShishirBharathi, Gaurang Mehta, and Karan Vahi, “Characterizing and profiling scientific workflows”, Future Generation Computer Systems, Vol. 29, Issue 3 , 2013: 682-692.
7.
Fan Zhang, Junwei Cao, Kai Hwang,Keqin Li, and Samee U. Khan, “Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimization”, IEEE Transaction on cloud computing , Vol 3, No. 2, April/June 2015, pp 156-168.
8.
Tongyi Zheng and Weili Luo, “An Improved Squirrel Search Algorithm for Optimization”, Hindawi Complexity Volume 2019, Article ID 6291968, 31 pages
9.
Yong Zhao, IoanRaicu, Shiyong Lu, Wenhong Tian, Heng Liu, Enabling scalable scientific workflow management in the Cloud”, Future Generation Computer Systems, 23 October 2014.
10.
Heyang Xu, Bo Yang, Weiwei Qi and Emmanuel Ahene, “A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery”, KSII Transations on internet and information system vol. 10, NO 3, Mar. 2016.
11.
Ahmad, Z.; Jehangiri, A.I Ala’anzy, M.A. Othman, M.Umar, A.I. “Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing”, Sensors 2021, 21, 7238.
12.
Zhongjin Li, Jiacheng Yu, Haiyang Hu, Jie Chen, Hua Hu, Jidong Ge and Victor Chang, “Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud”, The 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 95-104 ISBN: 978-989-758-296-7
13.
J. KokKonjaang and Lina Xu, “Multi-objective workflow optimization strategy (MOWOS) for cloud computing”, Journal of Cloud Computing: Advances, Systems and Applications, (2021) 10:11
14.
Yuandou Wang, Hang Liu, Wanbo Zheng, Yunni Xia, Yawen Li, Peng Chen, KunyinGuo, and Hong Xie, “Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning”, Special Section on Mobile Service Computing with Internet of Things, February 11, 2019.
15.
T. Prem Jacob, K. Pradeep, “A Multi-Objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization”, Wireless Personal Communications • November 2019 DOI: 10.1007/s11277-019-06566-w.
16.
Amandeep Varma, SakshiKushal, “A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling”, Parallel computing volume 62, February 2017, Pages 1-19.
17.
Neeraj Arora, Rohitash Kumar Banyal, “HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 10, 2020.
