Efficient Task Scheduling in Cloud Systems with Adaptive Discrete Chimp Algorithm

Year 2022, Volume: 10 Issue: 3, 328 - 336, 30.07.2022

Emrullah Gündüzalp , Güngör Yıldırım , Yetkin Tatar

https://doi.org/10.17694/bajece.989467

Cited By: 1

Abstract

Successful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.

Keywords

Chimp algorithm, Cloud computing, Makespan, Metaheuristic, Optimization

References

• [1] Strumberger, I., Tuba, E., Bacanin, N., & Tuba, M. (2019, June). Dynamic tree growth algorithm for load scheduling in cloud environments. In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 65-72). IEEE.
• [2] Avram, M. G. (2014). Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12, 529-534.
• [3] Abdullahi, M., & Ngadi, M. A. (2016). Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640-650.
• [4] Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021). Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation, 62, 100841.
• [5] Khishe, M., & Mosavi, M. R. (2020). Chimp optimization algorithm. Expert systems with applications, 149, 113338, https://doi.org/10.1016/j.eswa.2020.113338
• [6] Pradhan, A., Bisoy, S. K., & Das, A. (2021). A survey on pso based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University-Computer and Information Sciences.
• [7] Saurav, S. K., & Benedict, S. (2021, January). A Taxonomy and Survey on Energy-Aware Scientific Workflows Scheduling in Large-Scale Heterogeneous Architecture. In 2021 6th International Conference on Inventive Computation Technologies (ICICT) (pp. 820-826). IEEE.
• [8] Alsaidy, S. A., Abbood, A. D., & Sahib, M. A. (2020). Heuristic initialization of PSO task scheduling algorithm in cloud computing. Journal of King Saud University-Computer and Information Sciences.
• [9] Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., & Zivkovic, M. (2019, November). Task scheduling in cloud computing environment by grey wolf optimizer. In 2019 27th Telecommunications Forum (TELFOR) (pp. 1-4). IEEE.
• [10] [Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., & Murphy, J. (2020). A woa-based optimization approach for task scheduling in cloud computing systems. IEEE Systems Journal, 14(3), 3117-3128.
• [11] Belgacem, A., Beghdad-Bey, K., & Nacer, H. (2018, October). Task scheduling optimization in cloud based on electromagnetism metaheuristic algorithm. In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) (pp. 1-7). IEEE.
• [12] Aziza, H., & Krichen, S. (2018). Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing. Computing, 100(2), 65-91.
• [13] Li, K., Xu, G., Zhao, G., Dong, Y., & Wang, D. (2011, August). Cloud task scheduling based on load balancing ant colony optimization. In 2011 sixth annual ChinaGrid conference (pp. 3-9). IEEE.
• [14] Chen, X., & Long, D. (2019). Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm. Cluster Computing, 22(2), 2761-2769.
• [15] Liu, C. Y., Zou, C. M., & Wu, P. (2014, November). A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. In 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (pp. 68-72). IEEE.
• [16] Tsai, C. W., Huang, W. C., Chiang, M. H., Chiang, M. C., & Yang, C. S. (2014). A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2(2), 236-250.
• [17] Malik, R. F., Rahman, T. A., Hashim, S. Z. M., & Ngah, R. (2007). New particle swarm optimizer with sigmoid increasing inertia weight. International Journal of Computer Science and Security, 1(2), 35-44.
• [18] Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S., & Abraham, A. (2011, October). Inertia weight strategies in particle swarm optimization. In 2011 Third world congress on nature and biologically inspired computing (pp. 633-640). IEEE.
• [19] Mafarja, M., Aljarah, I., Faris, H., Hammouri, A. I., Ala’M, A. Z., & Mirjalili, S. (2019). Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267-286.
• [20] Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in cognitive sciences, 9(5), 250-257.
• [21] Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41(1), 23-50.
• [22] Yildirim, G., Alatas, B. New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-020-02701-9
• [23] Koyuncu, H. GM-CPSO: A New Viewpoint to Chaotic Particle Swarm Optimization via Gauss Map. Neural Process Lett 52, 241–266 (2020). https://doi.org/10.1007/s11063-020-10247-2Electromagnetic Fields (300 Hz to 300 GHz), Environmental Health Criteria 137, World Health Organization, Geneva, Switzerland, 1993.