Enhancing Kubernetes-Based Microservices Deployment Efficiency Through DevOps and GitOps
Irvan Maulana(1*); Rusydi Umar(2); Anton Yudhana(3);
(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author
AbstractAn effective and resilient means to deploy microservices to Kubernetes is an ongoing challenge. This challenge becomes more difficult with ever increasingly complex application architectures. This research explored a DevOps model based on GitOps that integrates ArgoCD and GitLab CI/CD, as a means to create a more effective, resilient, and scalable deployment. Twelve microservices that were deployed in a controlled experimentation format were used in a comparative approach to previous deployment practices that only considered manual deployments. The results show an overall deployment time improvement of 40%. For the deployments that were executed incorrectly, ArgoCD ensures service availability leveraging its self-healing capabilities. During the computation of each run we also experienced system performance in a sustained high-load environment. Upon high demand, we experienced the desired autoscaling behavior requested, which resulted in higher service responsiveness. In comparison to previous studies, this research considered statistical analysis, while also looking at an aspect of real-world orchestration and networking efficiency while adopting Kubernetes. Altogether, this research gives organizations practical advice on how they may optimize their deployment pipelines for efficient, scalable and resilient microservices. KeywordsKubernetes, DevOps, Microservices, GitOps, CI/CD
|
Full Text:PDF |
Article MetricsAbstract view: 214 timesPDF view: 83 times |
Digital Object Identifier![]() |
Cite |
References
S. Pallewatta, V. Kostakos, and R. Buyya, “MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments,” Journal of Systems and Software, vol. 209, Mar. 2024, doi: 10.1016/j.jss.2023.111910.
S. Alzide, “Cloud Computing: Evolution, Challenges, and Future Prospects,” Journal of Information Technology, Cybersecurity, and Artificial Intelligence, vol. 1, no. 1, pp. 52–63, Dec. 2024, doi: 10.70715/jitcai.2024.v1.i1.007.
K. Vishnivetskii, “Dynamic Scaling and Performance Optimization for Microservices using Kubernetes,” Asian Journal of Research in Computer Science, vol. 18, no. 3, pp. 213–220, Feb. 2025, doi: 10.9734/ajrcos/2025/v18i3587.
S. Hassan, R. Bahsoon, and R. Buyya, “Systematic scalability analysis for microservices granularity adaptation design decisions,” Softw Pract Exp, vol. 52, Jan. 2022, doi: 10.1002/spe.3069.
K. Q. Pham and T. Kim, “Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing,” Future Generation Computer Systems, vol. 158, pp. 501–515, Sep. 2024, doi: 10.1016/j.future.2024.04.047.
L. A. Vayghan, M. A. Saied, M. Toeroe, and F. Khendek, “A Kubernetes controller for managing the availability of elastic microservice based stateful applications,” Journal of Systems and Software, vol. 175, May 2021, doi: 10.1016/j.jss.2021.110924.
S. Rama Krishna, J. Srinivasa Rao, Y. Venkata Durga, L. Prem Venkatesh, and P. Sridhar, “Enhancing Software Deployment Efficiency: A Comparative Analysis of Agile Application Deployment Using CI/CD Pipelines,” 2024.
G. Hyun, J. Oak, D. Kim, and K. Kim, “The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment,” Sensors, vol. 24, no. 18, Sep. 2024, doi: 10.3390/s24186002.
K. Sakinala, “Advancements in Devops: The Role Of Gitops In Modern Infrastructure Management,” International Journal Of Information Technology And Management Information Systems, vol. 16, no. 1, pp. 632–646, Feb. 2025, doi: 10.34218/IJITMIS_16_01_045.
A. Kumar, M. Nadeem, and M. Shameem, “Machine learning based predictive modeling to effectively implement DevOps practices in software organizations,” Automated Software Engineering, vol. 30, Jul. 2023, doi: 10.1007/s10515-023-00388-8.
V. M. Tamanampudi, “Distributed Learning and Broad Applications in Scientific Research Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems,” 2019.
N. Vemuri, V. Manoj Tatikonda, and N. Thaneeru, “Integrating Deep Learning with DevOps for Enhanced Predictive Maintenance in the Manufacturing Industry,” 2022.
S. Ugale and A. Potgantwar, “Container Security in Cloud Environments: A Comprehensive Analysis and Future Directions for DevSecOps †,” Engineering Proceedings, vol. 59, no. 1, 2023, doi: 10.3390/engproc2023059057.
L. Prates and R. Pereira, “DevSecOps practices and tools,” Int J Inf Secur, vol. 24, no. 1, Feb. 2025, doi: 10.1007/s10207-024-00914-z.
N. Vemuri, N. Thaneeru, and V. M. Tatikonda, “AI-Optimized DevOps for Streamlined Cloud CI/CD,” 2024. [Online]. Available: www.ijisrt.com504
G. Turin, A. Borgarelli, S. Donetti, F. Damiani, E. B. Johnsen, and S. L. Tapia Tarifa, “Predicting resource consumption of Kubernetes container systems using resource models,” Journal of Systems and Software, vol. 203, Sep. 2023, doi: 10.1016/j.jss.2023.111750.
E. Zahoor, M. Chaudhary, S. Akhtar, and O. Perrin, “A formal approach for the identification of redundant authorization policies in Kubernetes,” Comput Secur, vol. 135, Dec. 2023, doi: 10.1016/j.cose.2023.103473.
M. R. Saleh Sedghpour, C. Klein, and J. Tordsson, “An Empirical Study of Service Mesh Traffic Management Policies for Microservices,” in ICPE 2022 - Proceedings of the 2022 ACM/SPEC International Conference on Performance Engineering, Association for Computing Machinery, Inc, Apr. 2022, pp. 17–27. doi: 10.1145/3489525.3511686.
K. V. Palavesam, M. V. Krishnamoorthy, and A. S M, “A Comparative Study of Service Mesh Implementations in Kubernetes for Multi-cluster Management,” Journal of Advances in Mathematics and Computer Science, vol. 40, no. 1, pp. 1–16, Jan. 2025, doi: 10.9734/jamcs/2025/v40i11958.
A. Wiedemann, M. Wiesche, H. Gewald, and H. Krcmar, “Integrating development and operations teams: A control approach for DevOps,” Information and Organization, vol. 33, no. 3, Sep. 2023, doi: 10.1016/j.infoandorg.2023.100474.
J. Langerman and W. S. Leung, “The effect of outsourcing and insourcing on Agile and DevOps,” Journal of Information Technology Teaching Cases, 2023, doi: 10.1177/20438869231176841.
A. Raharjo, P. Andyartha, W. Wijaya, Y. Purwananto, D. Purwitasari, and N. Juniarta, Reliability Evaluation of Microservices and Monolithic Architectures. 2022. doi: 10.1109/CENIM56801.2022.10037281.
A. Nicolas-Plata, J. L. Gonzalez-Compean, and V. J. Sosa-Sosa, “A service mesh approach to integrate processing patterns into microservices applications,” Cluster Comput, vol. 27, no. 6, pp. 7417–7438, Sep. 2024, doi: 10.1007/s10586-024-04342-5.
M. Waseem, P. Liang, and M. Shahin, “A Systematic Mapping Study on Microservices Architecture in DevOps,” Journal of Systems and Software, vol. 170, Dec. 2020, doi: 10.1016/j.jss.2020.110798.
D. Faustino, N. Gonçalves, M. Portela, and A. Rito Silva, “Stepwise migration of a monolith to a microservice architecture: Performance and migration effort evaluation,” Performance Evaluation, vol. 164, May 2024, doi: 10.1016/j.peva.2024.102411.
S. C and M. S, Security Aware Resource Management Framework (SARMF) for Edge-Cloud Computing. 2023. doi: 10.1109/ICOEI56765.2023.10125845.
J. B. Adelusi, “Kubernetes for Microservices Deployment Across Cloud Platforms.” .
D. D. Vu, M. N. Tran, and Y. Kim, “Predictive Hybrid Autoscaling for Containerized Applications,” IEEE Access, vol. 10, pp. 109768–109778, 2022, doi: 10.1109/ACCESS.2022.3214985.
P. Priya Patharlagadda, “Kubernetes Traffic Management using Istio,” Journal of Media & Management, pp. 1–4, Feb. 2022, doi: 10.47363/JMM/2022(4)E101.
M. Chigurupati and A. Jagtap, “Enhancing Microservice Resiliency and Reliability on Kubernetes with Istio: A Site Reliability Engineering Perspective,” International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 17–22, Nov. 2024, doi: 10.14445/22312803/IJCTT-V72I11P103.
A. Malhotra, A. Elsayed, R. Torres, S. Venkatraman, and A. S. Kaul, “Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Evaluate Canary Deployment Techniques using Kubernetes, Istio and Liquibase for Cloud Native Enterprise Applications to Achieve Zero Downtime for Continuous Deployments,” no. 1, 2017, doi: 10.1109/ACCESS.2024.07512.
K. Sakinala, “Advancements in Devops: The Role of Gitops in Modern Infrastructure Management,” International Journal Of Information Technology And Management Information Systems, vol. 16, pp. 632–646, Feb. 2025, doi: 10.34218/IJITMIS_16_01_045.
Ramadoni, E. Utami, and H. al Fatta, Analysis on the Use of Declarative and Pull-based Deployment Models on GitOps Using Argo CD. 2021. doi: 10.1109/ICOIACT53268.2021.9563984.
B. Chandra Vadde and V. B. Munagandla, “Cloud-Native DevOps: Leveraging Microservices and Kubernetes for Scalable Infrastructure,” 2024.
P. Somasekaram, R. Calinescu, and R. Buyya, “High-Availability Clusters: A Taxonomy, Survey, and Future Directions”, doi: 10.48550/arXiv.2109.15139.
A. Singh, V. Singh, and A. Aggarwal, “Improving Business Deliveries for Micro-services-based Systems using CI/CD and Jenkins,” Journal of Mines Metals and Fuels, Dec. 2023, doi: 10.18311/jmmf/2023/33936.
T. Kormanik and J. Poruban, “Exploring GitOps: An Approach to Cloud Cluster System Deployment,” in ICETA 2023 - 21st Year of International Conference on Emerging eLearning Technologies and Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 318–323. doi: 10.1109/ICETA61311.2023.10344182.
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Irvan Maulana, Rusydi Umar, Anton Yudhana

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.