International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 12, No. 2, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 841KB)
Optimizing Load Balancing in Cloud-Based Healthcare Systems: Leveraging Linear Programming, Metaheuristics, and Queuing Models to Minimize Latency and Maximize Throughput
PDF (841KB), PP.34-50
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Author(s)
Elijah Falode
1,*
Mustapha Danjuma Suleiman
2
Rapheal Oladipo Fifelola
3
Adeel Shaikh Muhammad
4
Ravitheja Chinni
5
Index Terms
Cloud Computing, Load Balancing, Healthcare Systems, Optimization, Genetic Algorithm, Simulated Annealing, Queuing Theory, Stochastic Processes, Latency, Throughput, Task Completion Time, Performance Evaluation
Abstract
Optimizing load balancing in cloud-based healthcare systems is critical for improving system performance, particularly in terms of reducing latency, increasing throughput, and enhancing task completion time. This study investigates the impact of optimization algorithms, specifically Genetic Algorithm (GA) and Simulated Annealing (SA), on the efficiency of cloud resource allocation in healthcare applications. Additionally, we incorporate queuing theory and stochastic processes to model the task arrival and server load dynamics. By applying these optimization techniques, the system performance was evaluated, showing significant improvements in the key performance metrics. The results highlighted a 50% improvement in latency, 50% increase in throughput, and 25% reduction in task completion time. The optimized system demonstrated enhanced resource utilization, ensuring more efficient real-time data processing in cloud healthcare environments. The proposed approach shows promising results for future applications in dynamic healthcare workload management.
Cite This Paper
Elijah Falode, Mustapha Danjuma Suleiman, Rapheal Oladipo Fifelola, Adeel Shaikh Muhammad, Ravitheja Chinni, "Optimizing Load Balancing in Cloud-Based Healthcare Systems: Leveraging Linear Programming, Metaheuristics, and Queuing Models to Minimize Latency and Maximize Throughput", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.12, No.2, pp. 34-50, 2026. DOI: 10.5815/ijmsc.2026.02.03
Reference
[1]A. Jangra and N. Mangla, "An efficient load balancing framework for deploying resource scheduling in cloud-based healthcare systems," Measurement: Sensors, Elsevier, 2023. DOI: 10.1016/j.measen.2022.100551.
[2]I. Aqeel, M. Shuaib, S. B. Khan, and A. Almusharraf, "Cloud load balancing strategies for optimizing IoT applications in healthcare," Sensors, vol. 23, no. 11, p. 5349, 2023.
[3]F. Ramezani, J. Lu, and F. K. Hussain, "Task-based system load balancing in cloud computing using particle swarm optimization," Int. J. Parallel and Distributed Systems, Springer, 2014.
[4]A. Lee, J. Mhatre, R. K. Das, and M. Hong, "Hybrid mobile cloud computing architecture with load balancing for healthcare systems," Computers, Materials & Continua, 2023.
[5]R. R. Dornala and S. Ponnapalli, "An advanced cloud security and load balancing in healthcare systems," IEEE Systems Journal, 2023.
[6]A. N. Alvi, A. A. Malik, M. A. Javed, and M. B. Khan, "Efficient load balancing for blockchain-based healthcare systems in smart cities," Applied Sciences, vol. 13, no. 4, p. 2411, 2023.
[7]A. M. Jasim and H. Al-Raweshidy, "An adaptive SDN-based load balancing method for edge/fog-based real-time healthcare systems," IEEE Access, 2024.
[8]N. K. Rajpoot, P. Singh, and B. Pant, "Nature-inspired load balancing approach in cloud computing environment for smart healthcare," in Proc. 4th Int. Conf. on Computing, ACM, 2022.
[9]V. Arulkumar, M. Aruna, D. Prakash, and M. Amanullah, "A novel cloud-assisted framework for consumer IoT based on Lanner swarm optimization in smart healthcare systems," Multimedia Tools and Applications, 2024.
[10]E. Moharamkhani, R. B. Garmaroodi, and M. Darbandi, "Classification of load balancing optimization algorithms in cloud computing: a survey based on methodology," Wireless Personal Communications, Springer, 2024.
[11]N. R. Moparthi, G. Balakrishna, P. Chithaluru, and M. Kolla, "Improved energy-efficient cloud-optimized load-balancing for IoT frameworks," Heliyon, 2023.
[12]B. A. A. Alruwaili, M. Humayun, et al., "Dynamic load balancing for enhanced network performance in IoT-enabled smart healthcare with fog computing," J. Physics: Conference Series, 2021.
[13]S. P. R. M. and S. Bhattacharya, "Load balancing of energy cloud using wind-driven and firefly algorithms in the internet of everything," J. Parallel and Distributed Computing, Elsevier, 2020.
[14]Z. Lu and X. Deng, "A cloud and IoT-enabled workload-aware healthcare framework using ant colony optimization algorithm," J. Advanced Computer Science and Applications, 2023.
[15]F. M. Talaat, H. A. Ali, M. S. Saraya, and A. I. Saleh, "Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO," Springer, 2021.
[16]M. A. Ala'anzy, R. Zhanuzak, and R. Akhmedov, "Fog-based architecture and load balancing methodology for health monitoring systems," IEEE Access, 2021.
[17]S. Dalal, K. Solanki, S. Dalal, and U. K. Lilhore, "A systematic literature review for load balancing and task scheduling techniques in cloud computing," Artificial Intelligence in Health Care, Springer, 2024.
[18]M. Kolla, N. R. Moparthi, P. Chithaluru, and M. Humayun, "Proposing a load balancing algorithm for cloud computing applications," J. Physics: Conference Series, 2021.
[19]A. Lee, J. Mhatre, R. K. Das, and M. Hong, "Hybrid mobile cloud computing architecture with load balancing for healthcare systems," Computers, Materials & Continua, 2023.
[20]M. K. Ahmed, S. A. Aliesawi, and O. Y. Abdulhammed, "Healthcare task allocation in cloud-based system based on an improved grey wolf optimization by angular acceleration concept," Engineering & Systems, 2024.
[21]A. Jangra and N. Mangla, "An efficient load balancing framework for deploying resource scheduling in cloud-based healthcare systems," Measurement: Sensors, 2023.
[22]I. Aqeel, S. B. Khan, M. Shuaib, and A. Almusharraf, "Load balancing using artificial intelligence for cloud-enabled internet of everything in healthcare," Sensors, vol. 23, no. 11, 2023.
[23]F. M. Talaat, H. A. Ali, M. S. Saraya, and A. I. Saleh, "Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO," Springer, 2022.
[24]V. Arulkumar et al., "A novel cloud-assisted framework for consumer IoT in smart healthcare systems," Multimedia Tools and Applications, 2024.
[25]A. Asghar, A. Abbas, and H. A. Khattak, "Fog-based architecture and load balancing methodology for health monitoring systems," IEEE Access, 2021.
[26]N. R. Moparthi et al., "Improved energy-efficient cloud-optimized load-balancing for IoT frameworks," Heliyon, 2023.
[27]A. M. Jasim and H. Al-Raweshidy, "An adaptive SDN-based load balancing method for edge/fog-based real-time healthcare systems," IEEE Systems Journal, 2024.
[28]E. Moharamkhani, R. B. Garmaroodi, and M. Darbandi, "Classification of load balancing optimization algorithms in cloud computing," Wireless Personal Communications, 2024.
[29]S. Kannan, "A novel approach towards achieving energy efficient and load balancing for wireless sensor network used in wearable physiological monitoring," Australian J. Basic and Applied Sciences, vol. 9, no. 11, pp. 221-228, 2015.
[30]C. A. Jaimez Olarte, "Nursing workload balancing: lean healthcare, analytics and optimization in two Latin American university hospitals," CORE, 2023.
[31]M. R. Friesen, R. D. McLeod, and T. Strome, "Load balancing at emergency departments using crowdinforming," J. Medical Systems, 2011.
[32]R. Logeswaran and L. C. Chen, "Node status algorithm for load balancing in distributed service architectures at paperless medical institutions," J. Medical Systems, 2012.
[33]P. B. Soundarabai and J. Thriveni, "Comparative study on load balancing techniques in distributed systems," Int. J. Information Technology and Knowledge Management, vol. 6, no. 1, 2012.
[34]Z. Chaczko and V. Mahadevan, "Availability and load balancing in cloud computing," in Proc. Int. Conf. Computer Science and Software Management, 2011.
[35]H. Shen and Y. Zhu, "Load rebalancing for distributed file systems in clouds," IEEE Trans. Parallel and Distributed Systems, 2012.
[36]F. Casati and M. Sayal, "Load balancing in distributed workflow management systems," in Proc. ACM Symp. Applied Computing, 2001.
[37]L. Cheung and Y. Kwok, "On load balancing approaches for distributed object computing systems," J. Supercomputing, vol. 15, 2004.
[38]X. Qin and H. Jiang, "A dynamic load balancing scheme for I/O-intensive applications in distributed systems," IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 6, 2003.
[39]G. Karypis and V. Kumar, "Load balancing across near-homogeneous multi-resource servers," in Proc. IEEE IPDPS, 2000.
[40]J. Cao and S. Jarvis, "Grid load balancing using intelligent agents," Parallel Computing, vol. 32, Elsevier, 2005.
[41]I. Riakiotakis and T. Andronikos, "Distributed dynamic load balancing for pipelined computations on heterogeneous systems," Parallel Computing, vol. 37, 2011.
[42]MIMIC-III Database, "Medical Information Mart for Intensive Care (MIMIC-III)," PhysioNet, 2021. Available: https://mimic.physionet.org/.
[43]A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. e215-e220, 2000.