Research Article

Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation

Volume: 7 Number: 2 August 31, 2024
EN

Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation

Abstract

Big data analysis used by Internet of Things (IoT) objects is one of the most difficult issues to deal with today due to the data increase rate. Container technology is one of the many technologies available to address this problem. Because of its adaptability, portability, and scalability, it is particularly useful in IoT micro-services. The most promising lightweight virtualization method for providing cloud services has emerged owing to the variety of workloads and cloud resources. The scheduler component is critical in cloud container services for optimizing performance and lowering costs. Even though containers have gained enormous traction in cloud computing, very few thorough publications address container scheduling strategies. This work organizes its most innovative contribution around optimization scheduling techniques, which are based on three meta-heuristic algorithms. These algorithms include the particle swarm algorithm, the genetic algorithm, and the ant colony algorithm. We examine the main advantages, drawbacks, and significant difficulties of the existing approaches based on performance indicators. In addition, we made a fair comparison of the employed algorithms by evaluating their performance through Quality of Service (QoS) while each algorithm proposed a contribution. Finally, it reveals a plethora of potential future research areas for maximizing the use of emergent container technology.

Keywords

Supporting Institution

Van Yuzuncu Yil University Scientific Research Projects Coordination Unit

Project Number

FYD-2022-10337

References

  1. I. Lee and K. Lee, “The Internet of Things (IoT): Applications, investments, and challenges for enterprises,” Bus. Horiz., vol. 58, no. 4, pp. 431–440, Jul. 2015, doi: 10.1016/J.BUSHOR.2015.03.008.
  2. W. W. W. Gartner, “ Gartner says the Internet of Things will transform the data center.”
  3. Y. Alahmad, T. Daradkeh, and A. Agarwal, “Availability-Aware Container Scheduler for Application Services in Cloud,” 2018 IEEE 37th Int. Perform. Comput. Commun. Conf. IPCCC 2018, Jul. 2018, doi: 10.1109/PCCC.2018.8711295.
  4. M. Alouane and H. El Bakkali, “Virtualization in Cloud Computing: Existing solutions and new approach,” Proc. 2016 Int. Conf. Cloud Comput. Technol. Appl. CloudTech 2016, pp. 116–123, Feb. 2017, doi: 10.1109/CLOUDTECH.2016.7847687.
  5. D. Merkel, “Docker: Lightweight Linux Containers for Consistent Development and Deployment”, Accessed: May 10, 2023. [Online]. Available: http://www.docker.io
  6. X. Li, P. Garraghan, X. Jiang, Z. Wu, and J. Xu, “Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 6, pp. 1317–1331, Jun. 2018, doi: 10.1109/TPDS.2017.2688445.
  7. M. Lin, J. Xi, W. Bai, and J. Wu, “Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud,” IEEE Access, vol. 7, pp. 83088–83100, 2019, doi: 10.1109/ACCESS.2019.2924414.
  8. B. Liu, J. Li, W. Lin, W. Bai, P. Li, and Q. Gao, “K-PSO: An improved PSO-based container scheduling algorithm for big data applications,” Int. J. Netw. Manag., vol. 31, no. 2, p. e2092, Mar. 2021, doi: 10.1002/NEM.2092.

Details

Primary Language

English

Subjects

Computer Software , Software Architecture

Journal Section

Research Article

Early Pub Date

August 23, 2024

Publication Date

August 31, 2024

Submission Date

March 13, 2024

Acceptance Date

June 28, 2024

Published in Issue

Year 2024 Volume: 7 Number: 2

APA
Koca, M., & Avcı, İ. (2024). Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation. Sakarya University Journal of Computer and Information Sciences, 7(2), 173-186. https://doi.org/10.35377/saucis...1452049
AMA
1.Koca M, Avcı İ. Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation. SAUCIS. 2024;7(2):173-186. doi:10.35377/saucis.1452049
Chicago
Koca, Murat, and İsa Avcı. 2024. “Optimization Planning Techniques With Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation”. Sakarya University Journal of Computer and Information Sciences 7 (2): 173-86. https://doi.org/10.35377/saucis. 1452049.
EndNote
Koca M, Avcı İ (August 1, 2024) Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation. Sakarya University Journal of Computer and Information Sciences 7 2 173–186.
IEEE
[1]M. Koca and İ. Avcı, “Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation”, SAUCIS, vol. 7, no. 2, pp. 173–186, Aug. 2024, doi: 10.35377/saucis...1452049.
ISNAD
Koca, Murat - Avcı, İsa. “Optimization Planning Techniques With Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation”. Sakarya University Journal of Computer and Information Sciences 7/2 (August 1, 2024): 173-186. https://doi.org/10.35377/saucis. 1452049.
JAMA
1.Koca M, Avcı İ. Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation. SAUCIS. 2024;7:173–186.
MLA
Koca, Murat, and İsa Avcı. “Optimization Planning Techniques With Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, Aug. 2024, pp. 173-86, doi:10.35377/saucis. 1452049.
Vancouver
1.Murat Koca, İsa Avcı. Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation. SAUCIS. 2024 Aug. 1;7(2):173-86. doi:10.35377/saucis. 1452049

Cited By

 

INDEXING & ABSTRACTING & ARCHIVING

 

31045 31044   ResimLink - Resim Yükle  31047 

31043 28939 28938 34240
 

 

29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License