Abstract
Cloud computing is a relatively new computing technology, which provides online on-demand computing services to cloud users. Task scheduling plays a crucial role in the cloud model. An efficient task allocation method, results with better resource utilization, have an impact on the quality of service, the overall performance, and user experience. The task scheduling should be carried out on multiple criteria, which is a difficult optimization problem and belongs to the class of NP-hard optimization problem. As the complexity of the problem increases, the exhaustive search becomes enormous. Consequently, an optimization technique is needed that can find the approximate solution in less amount of time. In this paper, we propose a hybridized bat optimization algorithm for multi-objective task scheduling. The simulations are performed in the CloudSim toolkit using standard parallel workloads, and the obtained results show that the proposed technique gives better results than other similar methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdullahi, M., Ngadi, M.A., Dishing, S.I., Abdulhamid, S.M., Ahmad, B.I.: An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment. J. Netw. Comput. Appl. 133, 60–74 (2019)
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., Zivkovic, M.: Task scheduling in cloud computing environment by grey wolf optimizer. In: 2019 27th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2019)
Bacanin, N., Tuba, E., Bezdan, T., Strumberger, I., Tuba, M.: Artificial flora optimization algorithm for task scheduling in cloud computing environment. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 437–445. Springer (2019)
Bacanin, N., Tuba, M.: Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators. Stud. Inform. Control 21(2), 137–146 (2012)
Bezdan, T., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Automatically designing convolutional neural network architecture with artificial flora algorithm. In: ICT Systems and Sustainability, pp. 371–378. Springer (2020)
Cheng, L., Wu, X.H., Wang, Y.: Artificial flora (AF) optimization algorithm. Appl. Sci. 8, 329 (2018). https://doi.org/10.3390/app8030329
Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)
Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Bare bones fireworks algorithm for the RFID network planning problem. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, July 2018. https://doi.org/10.1109/CEC.2018.8477990
Strumberger, I., Tuba, E., Bacanin, N., Tuba, M.: Dynamic tree growth algorithm for load scheduling in cloud environments. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 65–72, June 2019. https://doi.org/10.1109/CEC.2019.8790014
Strumberger, I., Tuba, M., Bacanin, N., Tuba, E.: Cloudlet scheduling by hybridized monarch butterfly optimization algorithm. J. Sens. Actuator Netw. 8(3), 44 (2019). https://doi.org/10.3390/jsan8030044
Tuba, E., Strumberger, I., Bezdan, T., Bacanin, N., Tuba, M.: Classification and feature selection method for medical datasets by brain storm optimization algorithm and support vector machine. Procedia Comput. Sci. 162, 307–315 (2019). (7th International Conference on Information Technology and Quantitative Management (ITQM 2019): Information Technology and Quantitative Management Based on Artificial Intelligence)
Tuba, M., Bacanin, N.: Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 499–506, May 2015. https://doi.org/10.1109/CEC.2015.7256931
Yang, X.S.: A New Metaheuristic Bat-Inspired Algorithm, pp. 65–74. Springer, Heidelberg (2010)
Acknowledgment
The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M. (2021). Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_83
Download citation
DOI: https://doi.org/10.1007/978-3-030-51156-2_83
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51155-5
Online ISBN: 978-3-030-51156-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)