Scheduling Tasks in the Cloud Computing Environment with the Effect of Cuckoo Optimization Algorithm

International Journal of Computer Science and Engineering
© 2016 by SSRG - IJCSE Journal
Volume 3 Issue 8
Year of Publication : 2016
Authors : Mohammad Javad Abbasi, Mehrdad Mohri

How to Cite?

Mohammad Javad Abbasi, Mehrdad Mohri, "Scheduling Tasks in the Cloud Computing Environment with the Effect of Cuckoo Optimization Algorithm," SSRG International Journal of Computer Science and Engineering , vol. 3,  no. 8, pp. 1-9, 2016. Crossref,


Cloud computing is a new computing way that has emerged recently in the commercial market Increased processor speed, storage technology growth and success of the Internet in the computing resources cheaper, more powerful and more accessible, make a new type of service on the Internet is called cloud computing. Big companies like Google, Amazon and Microsoft moved to this technology for more advantages. In this research will be discussed tasks scheduling optimization in cloud by cuckoo algorithm. Cuckoo optimization algorithm is a new way that can find the global optimum. This is one of the newest and most powerful optimization methods that have been introduced. This study aimed to minimize the overall execution time or cost time and improve load balancing and application resources with cloud computing is an algorithm for scheduling problem. The research is divided into two parts. In the first part will be reviewed a comprehensive study in the field of cloud computing in various aspects of job scheduling procedures Then in the second part to be evaluated the proposed methods to solve scheduling problems and to implement algorithms.


 Cloud computing, Cuckoo algorithms, optimization, scheduling tasks.


1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
2. Chen, Q., & Deng, Q. (2009). Cloud computing and its key techniques. Journal of Computer Applications, 29(9), 2565.
3. Dillon, T., Wu, C., & Chang, E. (2010). Cloud computing: issues and challenges. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on (pp. 27-33). Ieee.
4. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGAII. Evolutionary Computation, IEEE Transactions on, 6(2), 182- 197.
5. Cloud, D. M., Kelly, K. F., Bonaccorsi, D. P., & Weeks, M. K. (1997). U.S. Patent No. 5,634,127. Washington, DC: U.S. Patent and Trademark Office.
6. Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2009). Energy-efficient scheduling of HPC applications in cloud computing environments.arXiv preprint arXiv:0909.1146
7. Gong, L., Xie, J., Li, X., & Deng, B. (2013). Study on energy saving strategy and evaluation method of green cloud computing system. In Industrial Electronics and Applications (ICIEA), 2013 8th IEEE Conference on (pp. 483-488). IEEE.
8. Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861-874.
9. Lee, G., Chun, B. G., & Katz, R. H. (2011, June). Exploiting Heterogeneity in the Public Cloud for Cost-Effective Data Analytics. In 3rd Workshop on Hot Topics in Cloud Computing.
10. K. Akbari, M. & Javan, M.S. (2009), cloud computing, Research center of Amirkabir university
11. Kemp, R., Palmer, N., Kielmann, T., & Bal, H. (2010). Cuckoo: a computation offloading framework for smartphones. In Mobile Computing, Applications, and Services (pp. 59-79). Springer Berlin Heidelberg.
12. Koochaki, A., Skandarnezhad, A., Mohammadmoradi, Y., & Salimi, S. (2009), Multi-Machine Power System Fuzzy Stabilizer Design using Cuckoo Search Algorithm. Organ, 3, 16.
13. Lee, G. (2012). Resource allocation and scheduling in heterogeneous cloud environments. University of california.
14. Ling, W., Hang, N., & Li, R. (2013). Short-term wind power forecasting based on cloud SVM model [J]. Electric Power Automation Equipment, 7, 007.
15. Mark, C. C. T., Niyato, D., & Chen-Khong, T. (2011). Evolutionary optimal virtual machine placement and demand forecaster for cloud computing. In Advanced Information Networking and Applications (AINA), 2011 IEEE International Conference on (pp. 348-355). IEEE.
16. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision support systems,51(1), 176-189.
17. Mell, P., & Grance, T. (2010). The NIST definition of cloud computing.Communications of the ACM, 53(6), 50.
18. Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: an overview. InCloud computing (pp. 626-631). Springer Berlin Heidelberg.
19. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied soft computing,11(8), 5508-5518.
20. Ranjan, R., Zhao, L., Wu, X., Liu, A., Quiroz, A., & Parashar, M. (2010). Peer-to-peer cloud provisioning: Service discovery and load-balancing. InCloud Computing (pp. 195-217). Springer London.
21. Saadatjoo, F., Nasri, H., (2012), Task timing in cloud computing, university of Yazd
22. Tasgetiren, M. F., Liang, Y. C., Sevkli, M., & Gencyilmaz, G. (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, 177(3), 1930-1947.
23. Toroghi, H.A, Mohammadzade, S., (2012), Cloud computing, university of Ghazvin
24. Wang, W., Zeng, G., Tang, D., & Yao, J. (2012). Cloud-DLS: Dynamic trusted scheduling for Cloud computing. Expert Systems with Applications,39(3), 2321-2329.
25. Wu, C. M., Chang, R. S., & Chan, H. Y. (2014). A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems, 37, 141-147.
26. Xing, B., & Gao, W. J. (2014). Imperialist Competitive Algorithm. In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms (pp. 203-209). Springer International Publishing.
27. Yuan, Q., Liu, Z., Peng, J., Wu, X., Li, J., Han, F. & Kong, S. (2011). A leasing instances based billing model for cloud computing. In Advances in Grid and Pervasive Computing (pp. 33-41). Springer Berlin Heidelberg.