Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060502.

Cloud workflow scheduling optimization research based on ant colony algorithm


Yongqiang Li, Da Chen, Junjie Hao

Corresponding Author:
Junjie Hao

Hemei Company, Henan Energy Group Co., Ltd., Hebi, China


In view of the fact that enterprises can improve productivity through workflows, the problem of huge number of multi-user workflows and unbalanced user load on cloud computing platforms is investigated, based on an ant colony algorithm for decision task optimisation. With the help of a colony intelligence algorithm, automatic scheduling of tasks and optimisation of resources are accomplished while satisfying the enterprise's needs, while meeting server time constraints and load balancing. The optimised cloud workflow scheduling algorithm builds an intelligent business system that is highly flexible and scalable, and three times faster in terms of processing speed than before the optimisation.


cloud computing; workflow; ant colony algorithm; task scheduling

Cite This Paper

Yongqiang Li, Da Chen, Junjie Hao. Cloud workflow scheduling optimization research based on ant colony algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 9-13. https://doi.org/10.25236/AJCIS.2023.060502.


[1] Zhang L, Chen Y, Sun R, et al, A task scheduling algorithm based on PSO for grid computing, International Journal of Computational Intelligence Research, Vol.4, No.1, 2008, pp.37- 43.

[2] Mboula J E N, Kamla V C, Djamegni C T. Cost-time trade-off efficient workflow scheduling in cloud [J].Simulation Modelling Practice and Theory, 2020, 103: 102107.

[3] Shen M, Tzeng G H, Liu D R. Multi-criteria task assignment in workflow management systems [C]//Proceedings of the 36th annual Hawaii international conference on system sciences.Big Island, HI, USA: IEEE, 2003. 

[4] Huang Z, Lu X, Duan H.A task operation model for resource allocation optimization in business process management [J]. IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, 2012, 42 (5): 1256- 1270.

[5] Farahnakian F, et al. Using ant colony system to consolidate VMs for green cloud computing [J]. IEEE Transactions on Services Computing, 2015, 8 (2): 187-198.

[6] Li Zhongjin, Ge Jidong, Yang Hongji, et al A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds [J] Future Generation Computer Systems, 2016, 65: 140- 152 DOI: 10.1016/j. future .2015. 12.014.

[7] Hu Haiyang, Li Zhongjin, Hu Hua, et al. Multi-objective scheduling for scientific workflow in multicloud environment [J]Journal of Network and Computer Applications, 2018, 114: 108- 122. DOl: 10. 1016/j.jnca. 2018.03.028.

[8] Peng Wuliang, Wang Cheng 'en. ACO for solving resource- constrained project scheduling problem [J]. Journal of System Simulation, 2009, 21 (7) : 1974-1978 

[9] Hu Haiyang, Zhang Xiaofei, Hu Hua, et al. Decision support method based on process mining [J]. Computer Integrated Manufacturing Systems, 2013, 19 (8): 1755-1770

[10] IBM. Google and IBM announced university initiative to addressinternet-scale computing challenge [EB/OL].http: //www-03.ibm.com/press/us/en/pressrelease/22414 wss, 2007 October.

[11] Shen M, Tzeng G H, Liu D R Multi-criteria task assignment in workflow management systems[C]. Proceedings of the36th Annual Hawaii International Conference on System Sciences Washington, D.C. USA: IEEE, 2003: 202b. DOI: 10.1109/HICSS .2003.1174458.

[12] Zhu Z, Zhang G, Li M, et al. Evolutionary multi-objective workflow scheduling in cloud[J]IEE Transactions on Parallel and Distributed Systems, 2015, 27(5): 1-20.

[13] Tao Fei, Feng Ying, Zhang Lin. CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware Cloud service scheduling [J]. Applied Soft Computing, 2016, 19(2): 264-279.

[14] Hai Z, Tao K, Zhang X. An approach to optimized resource scheduling algorithm for open-source cloud systems [C] / Fifth Annual China Grid Conference. IEEE Press, 2015: 124-129.

[15] J. L. Deneubourg, S. Aron, S. Goss, and J. M. Pasteels. The self-organizing exploratory pattern of the argentine ant [J]. Journal of Insect Behavior, 1990.3(2).