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.
IoT micro-services Container method Optimization algorithms Scheduling methods Meta- heuristic algorithms
Van Yuzuncu Yil University Scientific Research Projects Coordination Unit
FYD-2022-10337
FYD-2022-10337
Primary Language | English |
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Subjects | Computer Software, Software Architecture |
Journal Section | Articles |
Authors | |
Project Number | FYD-2022-10337 |
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 2024Volume: 7 Issue: 2 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License