Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods
Keywords:internet of things, cloud computing, computing containers, mathematical modeling, performance optimization, resource allocation
The integration of computing containers into Internet of Things (IoT) systems created a lot of challenges and opportunities in the connected devices and cloud computing industries. In this paper, the author proposed a mathematical modeling method to analyze and optimize the deployment of computing containers into an IoT-based ecosystem. By implementing mathematical modeling techniques, such as queuing theory, optimization algorithms, and statistical analysis, we aim to address key concerns related to resource allocation, workload distribution, and performance optimization. Proposed models take the dynamic nature of an IoT system, considering factors such as real-time data streams and varying workloads for the satisfaction of scalability requirements. The author aids in identifying the optimal placement strategies for computing containers, ensuring efficient resource utilization and workload balancing across the IoT network.
X. Zou, “Research on cloud computing task scheduling based on calculus mathematical equation,” In Highlights in Science, Engineering and Technology, vol. 9, 2022, pp. 218–226. Darcy & Roy Press Co. Ltd. https://doi.org/10.54097/hset.v9i.1779
P. R. Kaveri, & P. Lahande, “Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing,” In SN Computer Science, vol. 4, Issue 2, 2023. Springer Science and Business Media LLC. https://doi.org/10.54097/hset.v9i.1779
R. Tasneem, & M. A. Jabbar, “An Insight into Load Balancing in Cloud Computing," In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 2022, pp. 1125–1140. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2456-9_113
R. Alakbarov, “An Optimization Model for Task Scheduling in Mobile Cloud Computing,” In International Journal of Cloud Applications and Computing, vol. 12, Issue 1, pp. 1–17, 2022. IGI Global. https://doi.org/10.4018/IJCAC.297102
M. Skulysh, “Mathematical model for searching the optimal resources size for the virtual service node,” Advanced Information Systems, 2(2), 2018, pp. 30–34. https://doi.org/10.20998/2522-9052.2018.2.05
P. R. Kaveri, & V. Chavan, “Mathematical model for higher utilization of database resources in cloud computing,” In 2013 Nirma University International Conference on Engineering (NUiCONE). 2013 Nirma University International Conference on Engineering (NUiCONE), IEEE, 2013. https://doi.org/10.1109/NUiCONE.2013.6780095
Zico Mutum, A Mathematical Model for Securing Cloud Computing, 2015.
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