Factors Influencing the Utilization of Cloud Optimization Tools Among DevOps Engineers
Insights from a Software Development Company in Sri Lanka.
DOI:
https://doi.org/10.62598/JVA.10.1.4.4Keywords:
Devops, Cloud Optimization Tools, Cloud Computing, Cloud Optimization TechnologiesAbstract
Purpose:The purpose of this study is to investigate the reasons why DevOps engineers utilize cloud optimization technologies. The goal is to help decision-makers improve the utilization of cloud technology, ensure cost-effectiveness, and simplify management within the organization.
Design: Two hundred individuals were randomly selected from a pool of eight hundred DevOps experts for quantitative research. Ethical issues were given top priority throughout the study process.
Methodology: Rigorous regression and correlation studies were carried out with a 95% confidence level. The study delved into significant correlations between independent factors and the company's cloud optimisation.
Approach: The study focused on reducing maintenance costs, improving scalability, and efficient resource utilisation as major goals. Participants emphasised the significance of cost savings, scalability, and simpler management.
Findings: The research showed significant correlations between independent factors and the company's cloud optimisation.The findings suggest that firm decision-makers focus on reducing maintenance costs, improving scalability, and efficiently utilizing resources to help their DevOps teams successfully use cloud optimization technologies.
Originality:The study offers practical recommendations for organizational tactics that promote the efficient use of cloud technologies.It illuminates the unique preferences of DevOps engineers regarding cloud optimisation technologies.
References
6. Reference
1. Aktas, M., 2018. Hybrid cloud computing monitoring software architecture. Concurrency and Computation: Practice and Experience, 30(21), p.e4694..
2. Attaran, M. and Woods, J.,, 2018. Cloud Computing Technology: A Viable Option for Small and Medium-Sized Businesses. Journal of Strategic Innovation & Sustainability, 13(2)..
3. Battina, D., 2020. Devops, A New Approach To Cloud Development & Testing. International Journal of Emerging Technologies and Innovative Research, pp.2349-5162..
4. Brown, R. &. G. A., (2022). Simplifying Cloud Maintenance: The Role of Optimization Tools. Journal of Cloud Computing Advances, Challenges and Applications,. pp. 13(2), 45-60. .
5. Dillion., 2010. https://ieeexplore.ieee.org/abstract/document/5610586.
6. Fisher, D., & Kumar, S., (2020). Balancing Optimization and Maintenance in Cloud Computing Environments. International Journal of Cloud Applications and Computing,. pp. 10(4), 1-15. .
7. Gokarna, M. a. S. R., 2021. DevOps: a historical review and future works. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 366-371). IEEE..
8. Green, F., & Malik, S. , (2018). Achieving Scalability in Cloud Computing: An Automation Perspective. Computing Research Review,. pp. 22(3), 112-129..
9. Grossman., 2009. https://www.sciencedirect.com/science/article/abs/pii/S0167739X08001155Grossman.
10. Guşeilă, L. B. D. a. M. S. 2. A. D. t. f. m.-c. I. a. I. 2. I. C. o. S. a. I. i. I. E. (. (. 1.-6. I., 2019. Guşeilă, L.G., Bratu, D.V. and Moraru, S.A., 2019, August. DevOps transformation for multi-cloud IoT applications. In 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI) (pp. 1-6). IEEE..
11. Hamilton, J., & Webster, P. , (2019). Technology Adoption and Cloud Computing: A Framework for Understanding DevOps. Journal of Information Technology Theory and Application,. pp. 20(1), 39-58..
12. Houssein, E.H., Gad,, 2018. Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation, 62, p.100841..
13. Jindal, A. a. G. M., 2021. From devops to noops: Is it worth it?. In Cloud Computing and Services Science: 10th International Conference, CLOSER 2020, Prague, Czech Republic, May 7–9, 2020, Revised Selected Papers 10 (pp. 178-202). Springer Internat.
14. Kaur., e., 2021. Cloud computing: theory and practice. Morgan Kaufmann..
15. Khan., e., 2020. To move or not to move: Cost optimization in a dual cloud-based storage architecture. Journal of Network and Computer Applications, 75, pp.223-235..
16. Kim., e., 2018. . Machine learning based resource allocation of cloud computing in auction. Comput. Mater. Continua, 56(1), pp.123-135..
17. Lee, e., 2020. Blockchain based cloud computing: Architecture and research challenges. IEEE Access, 8, pp.205190-205205.
18. Mishra, A. N. R. S. K. a. S. R., 2018. A Critical Review on Service Oriented Architecture and its Maintainability. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future D.
19. Mishra, A.K., Nagpal, R., Seth, K. and Sehgal, R., , 2021, September. A Critical Review on Service Oriented Architecture and its Maintainability. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future D.
20. Mohammad, S., 2018. Streamlining DevOps automation for Cloud applications. International Journal of Creative Research Thoughts (IJCRT), ISSN, pp.2320-2882..
21. Pawar, N. L. U. a. A. N., 2017. A hybrid ACHBDF load balancing method for optimum resource utilization in cloud computing. International Journal of Scientific Research in Computer Science, Engineer-ing and Information Technology, 3307, pp..
22. Patel., e., 2020. . Streamlining DevOps automation for Cloud applications. International Journal of Creative Research Thoughts (IJCRT), ISSN, pp.2320-2882..
23. Patel, R., et al. , (2021). Strategic Cloud Optimization and Cost Reduction: A Quantitative Study. Cloud Computing Economics,. pp. 8(4), 77-89..
24. Singh, e., 2021. A Comparative Study of Maintainability versus Availability Index of Open Source Software. Indian Journal of Science and Technology, 12(12)..
25. Smith, L., & Johnson, M., (2020). Cost Reduction through Automation in Cloud Computing Environments. Journal of Cloud Services and Applications,. pp. 11(2), 200-215..
26. Suk, T. H. J. B. M. a. Z. Z., 2019. July. Failure-aware application placement modeling and optimization in high turnover DevOps environment. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) (pp. 115-123). IEEE..
27. Sunyaev, A. a. S. A., 2020. Cloud computing. Internet Computing: Principles of Distributed Systems and Emerging Internet-Based Technologies, pp.195-236..
28. Tan., e., 2019. To move or not to move: Cost optimization in a dual cloud-based storage architecture. Journal of Network and Computer Applications, 75, pp.223-235..
29. Thompson, H., & Li, F. , (2019). Enhancing Resource Utilization in Cloud Environments: An Optimization Approach. Journal of Network and Systems Management. pp. 27(3), 422-441..
30. Viegas, E., Santin, A., Bachtold, J., 2021. Enhancing service maintainability by monitoring and auditing SLA in cloud computing. Cluster Computing, 24, pp.1659-1674..
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Vallis Aurea
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors of papers for publishing in the journal agree under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.https://creativecommons.org/licenses/by-nc-nd/4.0/
