PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Awad, A. I., El-Hefnawy, N. A., & Abdel-Kader, H. M. (2015). Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, 920-929. https://doi.org/10.1016/j.procs.2015.09.064

• Babu, L. D. D., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing Journal, 13(5), 2292-2303. https://doi.org/10.1016/j.asoc.2013.01.025

• Caragiannis, I., Flammini, M., Kaklamanis, C., Kanellopoulos, P., & Moscardelli, L. (2011). Tight bounds for selfish and greedy load balancing. Algorithmica, 61, 606-637. https://doi.org/10.1007/s00453-010-9427-8

• Coady, Y., Hohlfeld, O., Kempf, J., McGeer, R., & Schmid, S. (2015). Distributed cloud computing: Applications, status quo, and challenges. Computer Communication Review, 45(2), 38-43. https://doi.org/10.1145/2766330.2766337

• Dave, S., & Maheta, P. (2014). Utilizing round robin concept for load balancing algorithm at virtual machine level in cloud environment. International Journal of Computer Applications, 94(4), 23-29. https://doi.org/10.5120/16332-5612

• Devi, D. C., & Uthariaraj, V. R. (2016). Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Scientific World Journal, 2016, Article 3896065. https://doi.org/10.1155/2016/3896065

• Fatima, S. G., Fatima, S. K., Sattar, S. A., Khan, N. A., & Adil, S. (2019). Cloud computing and load balancing. International Journal of Advanced Research in Engineering and Technology, 10(2), 189-209. https://doi.org/10.34218/IJARET.10.2.2019.019

• Goyal, T., Singh, A., & Agrawa, A. (2012). Cloudsim: Simulator for cloud computing infrastructure and modeling. Procedia Engineering, 38, 3566-3572. https://doi.org/10.1016/j.proeng.2012.06.412

• Javadpour, A., Sangaiah, A. K., Pinto, P., Ja’fari, F., Zhang, W., Abadi, A. M. H., & Ahmadi, H. R. (2023). An energy-optimized embedded load balancing using DVFS computing in cloud data centers. Computer Communications, 197, 255-266. https://doi.org/10.1016/j.comcom.2022.10.019

• Kapoor, S., & Dabas, C. (2015). Cluster based load balancing in cloud computing. In 2015 8th International Conference on Contemporary Computing, IC3 2015 (pp. 76-81). IEEE Publishing. https://doi.org/10.1109/IC3.2015.7346656

• Kruekaew, B., & Kimpan, W. (2020). Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing. International Journal of Computational Intelligence Systems, 13(1), 496-510. https://doi.org/10.2991/ijcis.d.200410.002

• Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803-17818. https://doi.org/10.1109/ACCESS.2022.3149955

• Kumar, K. P., Ragunathan, T., Vasumathi, D., & Prasad, P. K. (2020). An efficient load balancing technique based on cuckoo search and firefly algorithm in cloud. International Journal of Intelligent Engineering and Systems, 13(3), 422-432. https://doi.org/10.22266/IJIES2020.0630.38

• Kumar, P., & Kumar, R. (2019). Issues and challenges of load balancing techniques in cloud computing: A survey. ACM Computing Surveys, 51(6), Article 120. https://doi.org/10.1145/3281010

• Li, G., & Wu, Z. (2019). Ant colony optimization task scheduling algorithm for SWIM based on load balancing. Future Internet, 11(4), Article 90. https://doi.org/10.3390/fi11040090

• Lu, Y., Zhang, J., Wu, S., Zhang, S., Zhang, Y., Li, Y., Ghosh, S., Banerjee, C., Kulkarni, A. K., Annappa, B., Domanal, S. G., Reddy, G. R. M., Komarasamy, D., & Muthuswamy, V. (2016). Load balancing in cloud environment using a novel hybrid scheduling algorithm. In 2015 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2015 (pp. 37-42). IEEE Publishing. https://doi.org/10.1109/CCEM.2015.31

• Mishra, S. K., Sahoo, B., & Parida, P. P. (2018). Load balancing in cloud computing: A big picture. Journal of King Saud University - Computer and Information Sciences, 32(2), 149-158. https://doi.org/10.1016/j.jksuci.2018.01.003

• Mohanty, S., Patra, P. K., Ray, M., & Mohapatra, S. (2017). A novel meta-heuristic approach for load balancing in cloud computing. International Journal of Knowledge-Based Organizations, 8(1), 29-49. https://doi.org/10.4018/ijkbo.2018010103

• Nerkar, M. H. (2012). Cloud computing in distributed system. International Journal of Computer Science and Informatics, 1(10), 97-101. https://doi.org/10.47893/ijcsi.2012.1072

• Paduraru, C. I. (2014). A greedy algorithm for load balancing jobs with deadlines in a distributed network. International Journal of Advanced Computer Science and Applications, 5(2), 56-59. https://doi.org/10.14569/ijacsa.2014.050209

• Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International Journal of Parallel Programming, 42(5), 739-754. https://doi.org/10.1007/s10766-013-0275-4

• Saura, J. R., Herraez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial brand communication analysis with a big data analytics technique. IEEE Access, 7, 37100-37108. https://doi.org/10.1109/ACCESS.2019.2905301

• Singh, H., Tyagi, S., & Kumar, P. (2021). Cloud resource mapping through crow search inspired metaheuristic load balancing technique. Computers and Electrical Engineering, 93, Article 107221. https://doi.org/10.1016/j.compeleceng.2021.107221

• Sinha, G., & Sinha, D. (2020). Enhanced weighted round robin algorithm to balance the load for effective utilization of resource in cloud environment. EAI Endorsed Transactions on Cloud Systems, 6(18), Article 166284. https://doi.org/10.4108/eai.7-9-2020.166284

• Sinha, U., & Shekhar, M. (2015). Comparison of various cloud simulation tools available in cloud computing. International Journal of Advanced Research in Computer and Communication Engineering, 4(3), 171-176. https://doi.org/10.17148/ijarcce.2015.4342

• Tawfeek, M. A., El-Sisi, A., Keshk, A. E., & Torkey, F. A. (2013). Cloud task scheduling based on ant colony optimization. In 2013 8th International Conference on Computer Engineering & Systems (ICCES) (pp. 64-69). IEEE Publishing. https://doi.org/10.1109/ICCES.2013.6707172

• Wang, Y. H., & Wu, I. C. (2009). Achieving high and consistent rendering performance of java AWT/Swing on multiple platforms. Software - Practice and Experience, 39(7), 701-736. https://doi.org/10.1002/spe