Abstract
Cloud computing is a relatively new computing paradigm that enables provision of storage and computing resources over a network to end-users. Task scheduling represents the allocation of tasks to be executed to the available resources. In this paper, we propose a scheduling algorithm, named artificial flora scheduler, with an aim to improve task scheduling in the cloud computing environments. The artificial flora belongs to the category of swarm intelligence metaheuristics that have proved to be very effective in solving NP hard problems, such as task scheduling. Based on the obtained simulation results and comparison with other approaches from literature, a conclusion is that the proposed scheduler efficiently optimizes execution of the submitted tasks to the cloud system, by reducing the makespan and the execution costs.
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References
Mell, P.M., Grance, T.: Sp 800–145. The NIST definition of cloud computing. Technical report, Gaithersburg, MD, United States (2011)
Cheng, L., Wu, X.-H., Wang, Y.: Artificial flora (AF) optimization algorithm. Appl. Sci. 8, 329 (2018)
Strumberger, I., Minovic, M., Tuba, M., Bacanin, N.: Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors 19(11), 2515 (2019)
Hrosik, R.C., Tuba, E., Dolicanin, E., Jovanovic, R., Tuba, M.: Brain image segmentation based on firefly algorithm combined with k-means clustering. Stud. Inf. Control 28(2), 167–176 (2019)
Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. Spec. Issue Comput. Intell. Metaheuristic Algorithms Appl. 2014, 16 (2014). Article ID 721521
Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., Tuba, M.: Mobile robot path planning by improved brain storm optimization algorithm. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, July 2018
Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Modified monarch butterfly optimization algorithm for RFID network planning. In: 2018 6th International Conference on Multimedia Computing and Systems, pp. 1–6 (2018)
Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., Tuba, M.: Designing convolutional neural network architecture by the firefly algorithm. In: 2019 International Young Engineers Forum (YEF-ECE), pp. 59–65, May 2019
Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)
Li, J., et al.: Task scheduling algorithm based on fireworks algorithm. EURASIP J. Wireless Commun. Netw. 2018, 256 (2018)
Sreenu, K., Sreelatha, M.: W-scheduler: whale optimization for task scheduling in cloud computing. Cluster Comput. (2017)
Acknowledgment
This work was supported by the Ministry of Education and Science of Republic of Serbia, Grant No. III-44006.
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Bacanin, N., Tuba, E., Bezdan, T., Strumberger, I., Tuba, M. (2019). Artificial Flora Optimization Algorithm for Task Scheduling in Cloud Computing Environment. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_47
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DOI: https://doi.org/10.1007/978-3-030-33607-3_47
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