Comparison of Automation Deployment Implementation on Google Cloud Virtual Machine Using Deployment Manager and Terraform
Abstract
Software development has significantly transformed in recent years, with organizations increasingly turning to cloud computing infrastructure to speed up and ease the development and implementation processes. Up until now, there are few studies testing the efficiency and performance of deployment automation using Google Cloud Deployment Manager, which is essentially a built-in infrastructure deployment service provided by Google Cloud Platform. Most studies tend to prefer using open-source software such as Terraform, Ansible, and Kubernetes. This research aims to compare the implementation of deployment automation using Google Cloud Deployment Manager and Terraform. Three parameters are used to compare the results of automation deployment implementation: deployment efficiency, website performance, and cost efficiency. The test results indicate that Google Cloud Deployment Manager outperforms Terraform in all three test parameters. Specifically, Google Cloud Deployment Manager demonstrated superior deployment efficiency, enhanced website performance, and better cost efficiency. Thus, it is concluded that Google Cloud Deployment Manager is a more effective solution for deployment automation compared to Terraform.
References
J. S. Hurwitz and D. Kirsch, Cloud Computing for Dummies. John Wiley & Sons., 2020.
K. Jamsa, Cloud Computing. Jones & Bartlett Learning, 2022.
D. C. Marinescu, Cloud computing: theory and practice. 2022.
K. Latha and T. Sheela, “Block based data security and data distribution on multi cloud environment,” J Ambient Intell Humaniz Comput, Jul. 2019, doi: 10.1007/s12652-019-01395-y.
M. Wurster et al., “Automating the Deployment of Distributed Applications by Combining Multiple Deployment Technologies,” in Proceedings of the 11th International Conference on Cloud Computing and Services Science, SCITEPRESS - Science and Technology Publications, 2021, pp. 178–189. doi: 10.5220/0010404301780189.
S. V. Kumar and M. K, “Estimating the Deployment Time for Cloud Applications using Novel Google Kubernetes Cloud Service over Microsoft Kubernetes Cloud Service,” J Pharm Negat Results, vol. 13, no. SO4, 2022, doi: 10.47750/pnr.2022.13.S04.186.
J. Shah and D. Dubaria, “Building Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, 2019, pp. 0184–0189. doi: 10.1109/CCWC.2019.8666479.
L. R. de Carvalho and A. Patricia Favacho de Araujo, “Performance Comparison of Terraform and Cloudify as Multicloud Orchestrators,” in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE, May 2020, pp. 380–389. doi: 10.1109/CCGrid49817.2020.00-55.
Y. Mansouri, V. Prokhorenko, and M. A. Babar, “An automated implementation of hybrid cloud for performance evaluation of distributed databases,” Journal of Network and Computer Applications, vol. 167, p. 102740, Oct. 2020, doi: 10.1016/j.jnca.2020.102740.
S. Sokolov, O. Idiriz, M. Vukadinoff, and S. Vlaev, “Scaling and Automation in Cloud Deployments of Enterprise Applications,” JOURNAL OF Engineering Science and Technology Review, 2020.
Y. Li, Z. Han, Q. Zhang, Z. Li, and H. Tan, “Automating Cloud Deployment for Deep Learning Inference of Real-time Online Services,” in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, IEEE, Jul. 2020, pp. 1668–1677. doi: 10.1109/INFOCOM41043.2020.9155267.
M. Malawski, A. Gajek, A. Zima, B. Balis, and K. Figiela, “Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions,” Future Generation Computer Systems, vol. 110, pp. 502–514, Sep. 2020, doi: 10.1016/j.future.2017.10.029.
H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J Bus Res, vol. 104, pp. 333–339, Nov. 2019, doi: 10.1016/j.jbusres.2019.07.039.
D. Novaliendry and V. D. Puteri, “E-Retail Percetakan Anambaleh Desain Menggunakan Framework Laravel,” Jurnal Teknologi Informasi dan Pendidikan, vol. 13, no. 1, 2020.
T. Sriwahyuni, O. Oktoria, and I. P. Dewi, “PENGEMBANGAN SISTEM INFORMASI MANAJEMEN PARIWISATA BERBASIS WEB,” Jurnal Teknologi Informasi dan Pendidikan, vol. 12, no. 1, pp. 92–99, Mar. 2019, doi: 10.24036/tip.v12i1.184.
T. Sriwahyuni and T. Surianto, “Design of Website-Based Information System for Psychology Service Unit of Universitas Negeri Padang Using YII2 Framework,” Jurnal Teknologi Informasi dan Pendidikan, 2023.
R. U. A. Sajid, S. Islam, A. B. K. Rakib, and A. Kaur, “Interpretation on the Google Cloud Platform and Its Wide Cloud Services,” International Journal of Security and Privacy in Pervasive Computing, vol. 14, no. 1, pp. 1–7, Nov. 2022, doi: 10.4018/IJSPPC.313586.
M. Zadka, DevOps in Python. Berkeley, CA: Apress, 2022. doi: 10.1007/978-1-4842-7996-0.
D. Muriyatmoko and Aziz Musthafa, “Website Performance Testing Using Speed Testing Model: A Case of Reputable Indonesian Journals,” Teknik: Jurnal Ilmu Teknik dan Informatika, vol. 2, no. 1, pp. 40–45, May 2022, doi: 10.51903/teknik.v2i1.120.
O. Alzakholi, L. Haji, H. Shukur, R. Zebari, S. Abas, and M. Sadeeq, “Comparison Among Cloud Technologies and Cloud Performance,” Journal of Applied Science and Technology Trends, vol. 1, no. 1, pp. 40–47, Apr. 2020, doi: 10.38094/jastt1219.
Y. SKA and J. P, “A Study And Analysis Of Continuous Delivery, Continuous Integration In Software Development Environment,” J Emerg Technol Innov Res, Sep. 2019.
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