Finding the Optimal Number of Computing Containers in IoT Systems: Application of Mathematical Modeling Methods
DOI:
https://doi.org/10.18372/1990-5548.76.17661Keywords:
internet of things, cloud computing, computing containers, mathematical modeling, performance optimization, resource allocationAbstract
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.
References
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.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
