Firefly Optimization Algorithm for Multi-Objective Job Scheduling in Cloud Computing
Keywords:
Firefly Optimization Algorithm, Internet of Things, Cloud Computing, Job Scheduling, Total Time Spent, Efficiency in Energy Use, Scalability and Multi-Objective OptimizationAbstract
Due to the increasing use of the Internet of Things, efficient task scheduling in cloud computing has become increasingly important with the aim of maximizing the use of available resources, reducing energy consumption, and enhancing the quality of service (QoS). In this paper, we use the Firefly Optimization (FFO) algorithm to improve scheduling efficiency and minimize the overall completion time in cloud environments. For this purpose, twelve distinct scenarios were designed in the Cooja Contiki simulator environment with the perspective of computationally intensive, input/output intensive, and mixed workloads, and the overall completion time results obtained with the Min-Min and GA-PSO-Min methods were compared and the better performance of the method was confirmed.
References
Abedinzadeh, M. H., & Akyol, E. (2023). A multidimensional opinion evolution model with confirmation bias. 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton),
Adaniya, M. H., Carvalho, L. F., Zarpelão, B. B., Sampaio, L. D., Abrão, T., Jeszensky, P. J. E., & Proença, M. L., Jr. (2015). Firefly Algorithm in Telecommunications. Bio-Inspired Computation in Telecommunications. https://doi.org/10.1016/B978-0-12-801538-4.00003-3
Adaniya, M. H., Lima, M. F., Rodrigues, J. J., Abrão, T., & Proença, M. L. (2012). Anomaly detection using dsns and firefly harmonic clustering algorithm. Proceedings of the 2012 IEEE International Conference on Communications (ICC), Ottawa, ON, Canada.
Ahmed, A. A., & Maheswari, D. (2017). Churn prediction on huge telecom data using hybrid firefly based classification. Egypt. Inform. J., 18, 215-220. https://doi.org/10.1016/j.eij.2017.02.002
Alazzam, H., Alhenawi, E., & Rizik, A. (2019). A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms. The Journal of Supercomputing, 75, 7994-8011. https://doi.org/10.1007/s11227-019-02936-0
Boroumand, A., Hosseini Shirvani, M., & Motameni, H. (2025). A heuristic task scheduling algorithm in cloud computing environment: an overall cost minimization approach. Cluster Computing, 28(2), 137. https://doi.org/10.1007/s10586-024-04843-3
Chen, M., Xu, J., Zhang, W., & Li, Z. (2025). A new customer-oriented multi-task scheduling model for cloud manufacturing considering available periods of services using an improved hyper-heuristic algorithm. Expert Systems with Applications, 269, 126419. https://doi.org/10.1016/j.eswa.2025.126419
Devaraj, A. F. S., Elhoseny, M., Dhanasekaran, S., Lydia, E. L., & Shankar, K. (2020). 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, 142, 36-45. https://doi.org/10.1016/j.jpdc.2020.03.022
Ghobaei-Arani, M., Jabbehdari, S., & Mohammad Ali, P. (2018). An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems, 78(1), 191-210. https://doi.org/10.1016/j.future.2017.02.022
Khaledian, N., Razzaghzadeh, S., Haghbayan, Z., & Völp, M. (2025). Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment. Sustainable Computing: Informatics and Systems, 45, 101077. https://doi.org/10.1016/j.suscom.2024.101077
Khezri, E., Yahya, R. O., Hassanzadeh, H., Mohaidat, M., Ahmadi, S., & Trik, M. (2024). DLJSF: data-locality aware job scheduling IoT tasks in fog-cloud computing environments. Results in Engineering, 21, 101780. https://doi.org/10.1016/j.rineng.2024.101780
Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2015). Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset. IEEE Commun. Surv. Tutor., 18, 184-208. https://doi.org/10.1109/COMST.2015.2402161
Kushwaha, S., & Singh, R. S. (2025). Deadline and budget-constrained archimedes optimization algorithm for workflow scheduling in cloud. Cluster Computing, 28(2), 117. https://doi.org/10.1007/s10586-024-04702-1
Lakshmana Rao, K., Sireesha, R., & Shanti, C. (2021). On the convergence and optimality of the firefly algorithm for opportunistic spectrum access. Int. J. Adv. Intell. Paradig., 18, 119. https://doi.org/10.1504/IJAIP.2021.112900
Liaquat, S., Saleem, O., & Azeem, K. (2020). Comparison of Firefly and Hybrid Firefly-APSO Algorithm for Power Economic Dispatch Problem. Proceedings of the IEEE 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), Bandung, Indonesia.
Liu, Z., Zhang, J., Li, Y., Bai, L., & Ji, Y. (2018). Joint jobs scheduling and lightpath provisioning in fog computing micro datacenter networks. J. Opt. Commun. Netw., 10(7), B152-B163. https://doi.org/10.1364/JOCN.10.00B152
Long, G., Wang, S., & Lv, C. (2025). QoS-aware resource management in cloud computing based on fuzzy meta-heuristic method. Cluster Computing, 28(4), 1-35. https://doi.org/10.1007/s10586-024-05021-1
Mahdi, M. S., & Hassan, N. F. (2018). Design of keystream Generator utilizing Firefly Algorithm. J. Al-Qadisiyah Comput. Sci. Math., 10, 91. https://jqcsm.qu.edu.iq/index.php/journalcm/article/view/441
Mokhtari, V., Mikaeilvand, N., Mirzaei, A., Nouri-moghaddam, B., & Gudakahriz, S. J. (2025). GA-PSO-MIN: A HYBRID HEURISTIC ALGORITHM FOR MULTI-OBJECTIVE JOB SCHEDULING IN CLOUD COMPUTING. Archives for Technical Sciences, 2(33), 22-46. https://doi.org/10.70102/afts.2025.1833.022
Murad, S. A., Azmi, Z. R. M., Muzahid, A. J. M., Bhuiyan, M. K. B., Saib, M., Rahimi, N., & Bairagi, A. K. (2024). SG-PBFS: Shortest gap-priority based fair scheduling technique for job scheduling in cloud environment. Future Generation Computer Systems, 150, 232-242. https://doi.org/10.1016/j.future.2023.09.005
Muradi, S. S., Badeel, R., Abdulkarim Alsandi, N. S., Alshaaya, R. F., Ahmed, R. A., Muhammed, A., & Derahman, M. (2022). Optimized MIN-MIN Task Scheduling Algorithm For Scientific Workflows In a Cloud Environment. Journal of Theoretical and Applied Information Technology, 100(2). https://www.researchgate.net/profile/Sallar-Murad/publication/358461191_OPTIMIZED_MIN-MIN_TASK_SCHEDULING_ALGORITHM_FOR_SCIENTIFIC_WORKFLOWS_IN_A_CLOUD_ENVIRONMENT/links/620385d4c2d279745e763540/OPTIMIZED-MIN-MIN-TASK-SCHEDULING-ALGORITHM-FOR-SCIENTIFIC-WORKFLOWS-IN-A-CLOUD-ENVIRONMENT.pdf
Pan, J. S., Yu, N., Chu, S. C., Zhang, A. N., Yan, B., & Watada, J. (2025). Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm. Computers, Materials and Continua, 82(2), 2495-2520. https://doi.org/10.32604/cmc.2024.058450
Paulraj, D., Sethukarasi, T., Neelakandan, S., Prakash, M., & Baburaj, E. (2023). An efficient hybrid job scheduling optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment. PLoS One, 18(3). https://doi.org/10.1371/journal.pone.0282600
Pradhan, A., Das, A., & Bisoy, S. K. (2025). Modified parallel PSO algorithm in cloud computing for performance improvement. Cluster Computing, 28(2), 131. https://doi.org/10.1007/s10586-024-04722-x
Reddy, G. N., & Kumar, S. P. (2017). Multi objective task scheduling algorithm for cloud computing using whale optimization technique. Proc. Int. Conf. Next Gener. Comput. Technol.,
Shandilya, S. K., Choi, B. J., Kumar, A., & Upadhyay, S. (2023). Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity. Processes, 11, 715. https://doi.org/10.3390/pr11030715
Sobhanayak, S., Kumar, T. A., & Sahoo, B. (2018). Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Computing and Informatics Journal, 3(2), 210-230. https://doi.org/10.1016/j.fcij.2018.03.004
Tuba, E., Tuba, M., & Beko, M. (2018). Two stage wireless sensor node localization using firefly algorithm. Smart Trends in Systems, Security and Sustainability, Singapore.
Yang, X. S. (2008). Nature-Inspired Metaheuristic Algorithms (Vol. 12). Luniver Press. https://books.google.com/books?hl=fa&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=Nature-Inspired+Metaheuristic+Algorithms&ots=DyfBqfCJrf&sig=CtlwBSGqpLY9SO8TbNGfoL_q1oc
Yu, G. (2020). A modified firefly algorithm based on neighborhood search. Concurr. Comput. Pract. Exp., 33, e6066. https://doi.org/10.1002/cpe.6066
Zade, B. M. H., Mansouri, N., & Javidi, M. M. (2025). An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud. Computers & Industrial Engineering, 200, 110836. https://doi.org/10.1016/j.cie.2024.110836
Zhang, H., Zou, Q., Ju, Y., Song, C., & Chen, D. (2022). Distance-based support vector machine to predict DNA N6-methyladenine modification. Curr. Bioinf., 17(5), 473-482. https://doi.org/10.2174/1574893617666220404145517
Zhao, Y., Liang, H., Zong, G., & Wang, H. (2023). Event-based distributed finite-horizon $ H_infty $ consensus control for constrained nonlinear multiagent systems. IEEE Syst. J., 32(12), 82-98. https://ieeexplore.ieee.org/abstract/document/10284586/
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Copyright (c) 2025 Vahid Mokhtari (Author); Nasser Mikaeilvand; Abbas Mirzaei, Babak Nouri-Moghaddam, Sajjad Jahanbakhsh Gudakahriz (Author)
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