Research Article
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Year 2022, , 35 - 43, 29.09.2022
https://doi.org/10.46810/tdfd.1123962

Abstract

References

  • [1] Strumberger I., Tuba E., Bacanin N., and Tuba, M. Dynamic tree growth algorithm for load scheduling in cloud environments. In 2019 IEEE Congress on Evolutionary Computation. 2019; p. 65-72.
  • [2] Avram MG. Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology.2014; 12, 529-534.
  • [3] Abdullahi M., Ngadi MA. Symbiotic organism search optimization-based task scheduling in cloud computing environment. Future Generation Computer Systems. 2016; 56, 640-650.
  • [4] Houssein EH., Gad AG., Wazery YM., Suganthan PN. Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation. 2021; 62, 100841.
  • [5] Mohamed AB. , Laila AF, Arun KS. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications; 2018.
  • [6] Yildirim G., Alatas B. New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches. Journal of Ambient Intelligence and Humanized Computing. 2021; 12, 9611–9635. https://doi.org/10.1007/s12652-020-02701-9
  • [7] Pradhan A., Bisoy SK., Das A. A survey on pso based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University-Computer and Information Sciences; 2021.
  • [8] Chou JS., Truong TN. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Applied Mathematics and Computation.2021; 389, 125535.
  • [9] Alsaidy SA., Abbood AD., Sahib MA. Heuristic initialization of PSO task scheduling algorithm in cloud computing. Journal of King Saud University-Computer and Information Sciences; 2020.
  • [10] Yıldırım S., Yıldırım G., Alatas B."Anlaşılabilir Sınıflandırma Kurallarının Ayçiçeği Optimizasyon Algoritması ile Otomatik Keşfi", Türk Doğa ve Fen Dergisi. 2021; vol. 10, no. 2, pp. 233-241, doi:10.46810/tdfd.976397
  • [11] Saurav SK., Benedict S. 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). 2021; (pp. 820-826). IEEE.
  • [12] Belgacem A., Beghdad-Bey K., Nacer H. Task scheduling optimization in cloud based on electromagnetism metaheuristic algorithm. In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) 2018; (pp. 1-7). IEEE.
  • [13] Yildirim G., Hallac İR., Aydin G., Tatar Y. "Running genetic algorithms on Hadoop for solving high dimensional optimization problems," 2015 9th International Conference on Application of Information and Communication Technologies (AICT). 2015; pp. 12-16, doi: 10.1109/ICAICT.2015.7338506
  • [14] Li K., Xu G., Zhao G., Dong Y., Wang D. Cloud task scheduling based on load balancing ant colony optimization. In 2011 sixth annual ChinaGrid conference. 2011; (pp. 3-9). IEEE.
  • [15] Liu CY., Zou CM., Wu P. 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 2014; (pp. 68-72). IEEE.
  • [16] Chen X., Çeng U., Liu K., Liu Q., Liu J., Ying Mao, Murphy J. A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems. 2020; Volume: 14, Issue: 3, 3117 – 3128. IEEE.
  • [17] Zavodnik D. Spatial aggregations of the swarming jellyfish Pelagia noctiluca (Scyphozoa), Mar. Biol. 1987; 94, 265–269.
  • [18] Kıran, MS., Fındık O. A directed artificial bee colony algorithm, Appl. Soft Comput. 2015; 26, 454–462.
  • [19] Kıran M.S., Gündüz M., Baykan ÖK. A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum, Appl. Math. Comput, 2012; C. 219, 1515–1521.
  • [20]Calheiros RN., Ranjan R., Beloglazov A., De Rose CA., Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 2011; 41(1), 23-50.
  • [21] Tsai CW., Huang WC., Chiang MH., Chiang MC., Yang CS. A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2014; 2, 236-250.
  • [22] Sasikaladevi N. Minimum makespan task scheduling algorithm in cloud computing, International Journal of Advances in Intelligent Informatics ISSN: 2442-6571, 2016; pp. 123-130

Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments

Year 2022, , 35 - 43, 29.09.2022
https://doi.org/10.46810/tdfd.1123962

Abstract

For cloud environments, task scheduling focusing on the optimal completion time (makespan) is vital. Metaheuristic approaches can be used to produce efficient solutions that will provide important cost savings to both the cloud service provider and the clients. On the other hand, since there is a high probability of getting stuck in local minima in metaheuristic solutions due to the type of problem, it may not always be possible to quickly reach the optimal solution. This study, using a metaheuristic approach, proposes a solution based on the Cloneable Jellyfish Algorithm for optimal task distribution in cloud environments. The unique feature of the proposed algorithm is that it allows dynamic population growth to be carried out in a controlled manner in order not to get stuck in local minima during the exploration phase. In addition, this algorithm, which uses a different cloning mechanism so that similar candidates are not generated in the population growth, has made it possible to achieve the optimal solution in a shorter time. To observe the solution performance, cloud environment simulations created in the Cloudsim simulator have been used. In experiments, the success of the proposed solution compared to classical scheduling algorithms has been proven.

References

  • [1] Strumberger I., Tuba E., Bacanin N., and Tuba, M. Dynamic tree growth algorithm for load scheduling in cloud environments. In 2019 IEEE Congress on Evolutionary Computation. 2019; p. 65-72.
  • [2] Avram MG. Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology.2014; 12, 529-534.
  • [3] Abdullahi M., Ngadi MA. Symbiotic organism search optimization-based task scheduling in cloud computing environment. Future Generation Computer Systems. 2016; 56, 640-650.
  • [4] Houssein EH., Gad AG., Wazery YM., Suganthan PN. Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation. 2021; 62, 100841.
  • [5] Mohamed AB. , Laila AF, Arun KS. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications; 2018.
  • [6] Yildirim G., Alatas B. New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches. Journal of Ambient Intelligence and Humanized Computing. 2021; 12, 9611–9635. https://doi.org/10.1007/s12652-020-02701-9
  • [7] Pradhan A., Bisoy SK., Das A. A survey on pso based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University-Computer and Information Sciences; 2021.
  • [8] Chou JS., Truong TN. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Applied Mathematics and Computation.2021; 389, 125535.
  • [9] Alsaidy SA., Abbood AD., Sahib MA. Heuristic initialization of PSO task scheduling algorithm in cloud computing. Journal of King Saud University-Computer and Information Sciences; 2020.
  • [10] Yıldırım S., Yıldırım G., Alatas B."Anlaşılabilir Sınıflandırma Kurallarının Ayçiçeği Optimizasyon Algoritması ile Otomatik Keşfi", Türk Doğa ve Fen Dergisi. 2021; vol. 10, no. 2, pp. 233-241, doi:10.46810/tdfd.976397
  • [11] Saurav SK., Benedict S. 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). 2021; (pp. 820-826). IEEE.
  • [12] Belgacem A., Beghdad-Bey K., Nacer H. Task scheduling optimization in cloud based on electromagnetism metaheuristic algorithm. In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) 2018; (pp. 1-7). IEEE.
  • [13] Yildirim G., Hallac İR., Aydin G., Tatar Y. "Running genetic algorithms on Hadoop for solving high dimensional optimization problems," 2015 9th International Conference on Application of Information and Communication Technologies (AICT). 2015; pp. 12-16, doi: 10.1109/ICAICT.2015.7338506
  • [14] Li K., Xu G., Zhao G., Dong Y., Wang D. Cloud task scheduling based on load balancing ant colony optimization. In 2011 sixth annual ChinaGrid conference. 2011; (pp. 3-9). IEEE.
  • [15] Liu CY., Zou CM., Wu P. 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 2014; (pp. 68-72). IEEE.
  • [16] Chen X., Çeng U., Liu K., Liu Q., Liu J., Ying Mao, Murphy J. A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems. 2020; Volume: 14, Issue: 3, 3117 – 3128. IEEE.
  • [17] Zavodnik D. Spatial aggregations of the swarming jellyfish Pelagia noctiluca (Scyphozoa), Mar. Biol. 1987; 94, 265–269.
  • [18] Kıran, MS., Fındık O. A directed artificial bee colony algorithm, Appl. Soft Comput. 2015; 26, 454–462.
  • [19] Kıran M.S., Gündüz M., Baykan ÖK. A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum, Appl. Math. Comput, 2012; C. 219, 1515–1521.
  • [20]Calheiros RN., Ranjan R., Beloglazov A., De Rose CA., Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 2011; 41(1), 23-50.
  • [21] Tsai CW., Huang WC., Chiang MH., Chiang MC., Yang CS. A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2014; 2, 236-250.
  • [22] Sasikaladevi N. Minimum makespan task scheduling algorithm in cloud computing, International Journal of Advances in Intelligent Informatics ISSN: 2442-6571, 2016; pp. 123-130
There are 22 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mücahit Bürkük 0000-0002-4974-0590

Güngör Yıldırım 0000-0002-4096-4838

Publication Date September 29, 2022
Published in Issue Year 2022

Cite

APA Bürkük, M., & Yıldırım, G. (2022). Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments. Türk Doğa Ve Fen Dergisi, 11(3), 35-43. https://doi.org/10.46810/tdfd.1123962
AMA Bürkük M, Yıldırım G. Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments. TDFD. September 2022;11(3):35-43. doi:10.46810/tdfd.1123962
Chicago Bürkük, Mücahit, and Güngör Yıldırım. “Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments”. Türk Doğa Ve Fen Dergisi 11, no. 3 (September 2022): 35-43. https://doi.org/10.46810/tdfd.1123962.
EndNote Bürkük M, Yıldırım G (September 1, 2022) Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments. Türk Doğa ve Fen Dergisi 11 3 35–43.
IEEE M. Bürkük and G. Yıldırım, “Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments”, TDFD, vol. 11, no. 3, pp. 35–43, 2022, doi: 10.46810/tdfd.1123962.
ISNAD Bürkük, Mücahit - Yıldırım, Güngör. “Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments”. Türk Doğa ve Fen Dergisi 11/3 (September 2022), 35-43. https://doi.org/10.46810/tdfd.1123962.
JAMA Bürkük M, Yıldırım G. Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments. TDFD. 2022;11:35–43.
MLA Bürkük, Mücahit and Güngör Yıldırım. “Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments”. Türk Doğa Ve Fen Dergisi, vol. 11, no. 3, 2022, pp. 35-43, doi:10.46810/tdfd.1123962.
Vancouver Bürkük M, Yıldırım G. Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments. TDFD. 2022;11(3):35-43.