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A New Task Scheduling Algorithm based on Water Wave Optimization for Cloud Computing

by Dina A. Amer, Gamal Attiya, Ibrahim Ziedan, Aida A. Nasr
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 3
Year of Publication: 2021
Authors: Dina A. Amer, Gamal Attiya, Ibrahim Ziedan, Aida A. Nasr
10.5120/ijca2021921320

Dina A. Amer, Gamal Attiya, Ibrahim Ziedan, Aida A. Nasr . A New Task Scheduling Algorithm based on Water Wave Optimization for Cloud Computing. International Journal of Computer Applications. 183, 3 ( May 2021), 65-75. DOI=10.5120/ijca2021921320

@article{ 10.5120/ijca2021921320,
author = { Dina A. Amer, Gamal Attiya, Ibrahim Ziedan, Aida A. Nasr },
title = { A New Task Scheduling Algorithm based on Water Wave Optimization for Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { May 2021 },
volume = { 183 },
number = { 3 },
month = { May },
year = { 2021 },
issn = { 0975-8887 },
pages = { 65-75 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number3/31912-2021921320/ },
doi = { 10.5120/ijca2021921320 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:49.166883+05:30
%A Dina A. Amer
%A Gamal Attiya
%A Ibrahim Ziedan
%A Aida A. Nasr
%T A New Task Scheduling Algorithm based on Water Wave Optimization for Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 3
%P 65-75
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays cloud computing provides many benefits for organizations. Businesses can ensure reliable calamity recovery and backup solutions without the spat of tuning them up on a physical machine. For many companies, exploiting complex calamity recovery plans can be an expensive guarantee, and backing up data is time exhaustion. The cloud itself is built in such a way that the data stored more than one time in servers, so that if any server fails, the data is backed up immediately. The capability of accessing data readily is available after handling the failure. However, still, cloud computing resources face many problems such as scheduling problems. This paper tackles the resource scheduling problem and presents a new efficient algorithm, called Improved Water Wave Optimization (IWWO), to address such a problem. The main idea is the enhancement/improvement of the Water Wave Optimization (WWO) algorithm by using reinforcement learning to overcome the local optimality of the conventional WWO during the searching process. The proposed IWWO is implemented in the CloudSim toolkit and evaluated by considering a real data set and a randomly generated data set. The results are compared with the results of the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. The obtained results show that the IWWO can solve the resource scheduling with minimum schedule length and a high balance degree.

References
  1. Zheng, Zibin, Jieming Zhu, and Michael R. Lyu. "Service-generated big data and big data-as-a-service: an overview", the IEEE BigData Congress, 2013.
  2. Alkhanak, E. Nabiel, S. P. Lee, R. Rezaei, and R. M. Parizi, “Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues”, Journal of Systems and Software, Vol.113, pp. 1-26, 2016.
  3. L. Mei, W.K. Chan, and T. H. Tse, “A tale of clouds: paradigm comparisons and some thoughts on research issues,” Proceedings of the IEEE Asia-Pacific Services Computing Conference (APSCC’08), pp. 464-469, 2008.
  4. B. Keshanchi and N.J. Navimipour, “Priority-based task scheduling in the cloud systems using a memetic algorithm”, Journal of Circuits, Systems and Computers 25(10) (2016), 1–33.
  5. F. Ramezani, J. Lu and F. Hussain, “Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization”, Proceeding of the International Conference on Service-Oriented Computing, Springer, 2013, pp. 237–251.T. Mathew, K. C. Sekaran, and J. Jose, “Study and analysis of various task scheduling algorithms in the cloud computing environment.” Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 658–664, 2
  6. S. K. Panda, I. Gupta, and P. K. Jana, “Allocation-aware task scheduling for heterogeneous multi-cloud systems,” Procedia Comput. Sci., vol. 50, pp.176–184, 2015.
  7. Zhang, J., Zhou, Y. and Luo, Q, “Nature-inspired approach: a wind-driven water wave optimization algorithm“, Appl. Intell., Vol. 49, pp. 233-252, 2019.
  8. S.K. Mishra, B. Sahoo and P. P. Parida, “Load balancing in cloud computing: A big picture”, Journal of King Saud University – Computer and Information Sciences, Vol. 32, pp.149–158, 2018.
  9. I. Strumberger , M. Tuba, N. Bacanin and E. Tuba, “Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm”, J. Sensor and Actuator Network, Vol. 8: doi:10.3390/jsan8030044 , 2019.
  10. A. A. Nasr, N. A. El-Bahnasawy, G. Attiya, and A. El-Sayed, “Using the TSP Solution Strategy for Cloudlet Scheduling in Cloud Computing”, Journal of Network and Systems Management, Vol. 27, Issue 2, pp. 366-387, 2019.
  11. F. Mahmoodi and K.Dooley “A comparison of exhaustive and non-exhaustive group scheduling heuristics in a manufacturing cell,” Int. J. Prod. Res, Vol. 29, pp. 1923–1939, 1991.
  12. S. Toumi, B. Jarboui, M. Eddaly, and A. Rebaı, “Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion,” Int. J. Math. Oper. Res, Vol. 10, pp. 34–48, 2017.
  13. B. Pavithra, and R. Ranjana, “A comparative study on performance of energy efficient load balancing techniques in cloud,” International Conference in Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1192–1196, 2016.
  14. S. H. H. Madni, M. S. Abd Latiff, M. Abdullahi, S. M. Abdulhamid, and M. J. Usman, “Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment,” PLoS One, vol. 12, no. 5, pp. 1–26, 2017, doi: 10.1371/journal.pone.0176321.
  15. G. Ming and H. Li, "An Improved Algorithm Based on Max-Min for Cloud Task Scheduling," Recent Advances in Computer Science and Information Engineering in Springer Berlin Heidelberg, vol. Volume 125 of the series Lecture Notes in Electrical Engineering, pp. 217-223, January 2012.
  16. O. M. Elzeki, M. Z. Reshad and M. A. Elsoud, “Improved Max-Min Algorithm in Cloud Computing”, International Journal of Computer Applications Vol. 50, pp.22-27, July 2012.
  17. H. Gamal El Din Hassan Ali, I. A. Saroit, and A. M. Kotb, “Grouped tasks scheduling algorithm based on QoS in cloud computing network,” Egypt. Informatics J., vol. 18, no. 1, pp. 11–19, 2017, doi: 10.1016/j.eij.2016.07.002.
  18. R. Zhang, F. Tian, X. Ren, Y. Chen, K. Chao, R. Zhao, B. Dong, W. Wang, “Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment,” Serv. Oriented Comput. Appl. 2018. https://doi.org/10.1007/s11761-018-0231-72018.
  19. D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm”, Journal of Global Optimization, Vol. 39, pp.459–471, 2007.
  20. M. Kalra and S. Singh, “A review of metaheuristic scheduling techniques in cloud computing,” Egypt. Informatics J., vol. 16, no. 3, pp. 275–295, 2015, doi: 10.1016/j.eij.2015.07.001.
  21. A. F.S. Devaraj, M. Elhoseny, S. Dhanasekaran, E. LaxmiLydia and K.Shankar,” Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments,” Journal of Parallel and Distributed Computing, Vol. 142, pp. 36-45, 2020.
  22. Keshanchi, Bahman, A. Souri, and N. J. Navimipour, “An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing”, Journal of Systems and Software, Vol. 124, pp. 1-21, 2017.
  23. S. A. Hamad and F.A. Omara, “Genetic-Based Task Scheduling Algorithm in Cloud Computing Environment,” International Journal of Advanced Computer Science and Applications, Vol. 7, pp. 550-556, 2016.
  24. K. L. D. S. Valli, “Multi ‑ objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment,” J. Supercomput., no. 0123456789, 2021, doi: 10.1007/s11227-020-03606-2
  25. M.Mezmaz, N. Melab, Y. Kessaci, Y. C. Lee, E.-G. Talbi, A.Y. Zomaya and D. Tuyttens, “A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems,” Journal of Parallel and Distributed Computing, Vol. 71,pp. 1497-1508, 2011.
  26. M. Dorigo, and L.M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput., Vol. 1, pp. 53–66, 1997.
  27. K. Li, G. Xu, G. Zhao, Y. Dong and D. Wang, "Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization," 2011 Sixth Annual Chinagrid Conference, Liaoning, 2011, pp. 3-9, doi: 10.1109/ChinaGrid.2011.17.
  28. K. Sreenu and S. Malempati, “MFGMTS: Epsilon Constraint-Based Modified Fractional Grey Wolf Optimizer for Multi-Objective Task Scheduling in Cloud Computing,” IETE J. Res., vol. 65, no. 2, pp. 201–215, 2019, doi: 10.1080/03772063.2017.1409087.
  29. I. Attiya, M. Abd Elaziz, and S. Xiong, “Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm,” Comput. Intell. Neurosci., vol. 2020, 2020, doi: 10.1155/2020/3504642.
  30. F. Hemasian-Etefagh and F. Safi-Esfahani, “Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing,” J. Supercomput., vol. 75, no. 10, pp. 6386–6450, 2019, doi: 10.1007/s11227-019-02832-7.
  31. Yu-Jun Zheng, “Water wave optimization: A new nature-inspired metaheuristic”, Computers &Operations Research, Vol. 55, pp.1–11, 2015.
  32. Jinzhong Zhang, Yongquan Zhou and Qifang Luo, “An improved sine cosine water wave optimization algorithm for global optimization,” Journal of Intelligent & Fuzzy Systems, Vol.34, pp. 2129–2141, 2018.
  33. Xiao-Bei Wu, Jie Liao and Zhi-Cheng Wang, “Water wave optimization for the traveling salesman problem” in: D.-S. Huang, V. Bevilacqua, P. Premaratne (Eds.), Intelligent Computing Theories and Methodologies, Springer, Cham, pp. 137–146.
  34. X. Yun, X. Feng, X. Lyu, S. Wang, and B. Liu, “A novel water wave optimization based memetic algorithm for flow-shop scheduling” IEEE Congress on Evolutionary Computation, pp. 1971–1976, 2016.
  35. A. Gosavi, “A tutorial for reinforcement learning,” Missouri University of Science and Technology, Tech. Rep., September 2019.
  36. T. Goyal, A. Singh and A. Agrawal, “Cloudsim: simulator for cloud computing infrastructure and modeling,” Procedia Engineering, Vol. 38, pp. 3566-3572, 2012.
  37. D. G. Feitelson, D. Tsafrir, D. Krakov, “Experience with using the parallel workloads archive,” J. Parallel Distrib. Comput. 74 (10), 2967–2982 (2014).
  38. http://www.cs.huji.ac.il/labs/parallel/workload/l_nasa_ipsc/index.html#usage.
  39. K. Jansen, K.-M. Klein, and J. Verschae, “Closing the gap for makespan scheduling via sparsification techniques,” arXiv preprint arXiv:1604.07153 (2016).
  40. A. V. Lakra and D. K. Yadav, “Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization,” Procedia Computer Science, vol. 48, pp. 107 – 113, 2015.
  41. A.A. Nasr, A. T. Chronopoulos, N.A. El-Bahnasawy, G. Attiya, and A. El-Sayed, “A Novel Water Pressure Change Optimization Technique for Solving Scheduling Problem in Cloud Computing” Journal of Cluster Computing, Vol. 22, Issue 2, pp. 601-617, 15 June 2019.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing task scheduling optimization and water wave optimization

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