Hi. We have experimented with genetic algorithms, tabu search, differential equation and harmony search. Genetic algorithms and harmony search found good. Please refer the papers for more details.

my respected sir thank you so much to give me feedback, will you provide links of these related papers ?

Dear qutub uddin,

Nowadays most of scheduling algorithms are based on meta heuristics or nature inspired like Particle swarm optimization and intelligent water drop, frog shuffled and grey wolf optimization etc., There will be lot of review papers are available especially for cloud scheduling task you just go through all for getting clear ideas.

http://ofmas.ir/dlpaper/broo.pdf -> Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm

regards

i am really thank full all of you, to share your information with me.

Velliangiri Sarveshwaran are you able to provide me links of these related papers ?

Sure I I'll update you

thanks !

Dear Qutub Uddin,

Here I have given you 12 reference papers belongs to only cloud scheduling tasks you just go through all. All the best

1.Elsherbiny, S., Eldaydamony, E., Alrahmawy, M., & Reyad, A. E. (2017). An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment. Egyptian Informatics Journal, 1–23. https://doi.org/10.1016/j.eij.2017.07.001

2.Raghavan, S., Sarwesh, P., Marimuthu, C., & Chandrasekaran, K. (2015). Bat algorithm for scheduling workflow applications in cloud. 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV), 139–144. https://doi.org/10.1109/EDCAV.2015.7060555

3.Mohamad, A. B., Zain, A. M., & Nazira Bazin, N. E. (2014). Cuckoo Search Algorithm for Optimization Problems—A Literature Review and its Applications. Applied Artificial Intelligence, 28(5), 419–448. https://doi.org/10.1080/08839514.2014.904599

4.Rodriguez, M. A., & Buyya, R. (2014). Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds. IEEE Transactions on Cloud Computing, 2(2), 222–235. https://doi.org/10.1109/TCC.2014.2314655

5.Kumar, B., Kalra, M., & Singh, P. (2017). Discrete Binary Cat Swarm Optimization for Scheduling Workflow Applications in Cloud Systems. 2017 3Rd Ieee International Conference on Computational Intelligence & Communication Technology (Cict), 1–6.

6.Yang, X. S., Karamanoglu, M., & He, X. (2014). Flower pollination algorithm: A novel approach for multiobjective optimization. Engineering Optimization, 46(9), 1222–1237. https://doi.org/10.1080/0305215X.2013.832237

7.Hoffa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., & Good, J. (2008). On the use of cloud computing for scientific workflows. Proceedings - 4th IEEE International Conference on EScience, EScience 2008, 640–645. https://doi.org/10.1109/eScience.2008.167

Samuel, G. G., & Rajan, C. C. A. (2014). Proceedings of the Third 8.International Conference on Soft Computing for Problem Solving, 258. https://doi.org/10.1007/978-81-322-1771-8

9.Kaur, P., & Mehta, S. (2017). Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. Journal of Parallel and Distributed Computing, 101, 41–50. https://doi.org/10.1016/j.jpdc.2016.11.003

10.Eusuff, M., Lansey, K., & Pasha, F. (2006). Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2), 129–154. https://doi.org/10.1080/03052150500384759

11.Herodotou, H., Lim, H., Luo, G., Borisov, N., & Dong, L. (2011). Starfish : A Self-tuning System for Big Data Analytics. Cidr, 11, 261–272. https://doi.org/10.1.1.222.6934

12.Blake, S. (2002). The ecology of forest elephant distribution and its implications for conservation. University of Edinburgh, Edinburgh, Scotland, 319. https://doi.org/10.1017/S0022112097006745

Velliangiri Sarveshwaran

I am really thankful to you !