Containerized Application Deployment on Microsoft Azure Kubernetes Service (AKS)
DOI:
https://doi.org/10.61424/6vvbmj66Keywords:
Containerization, Kubernetes, Microsoft Azure, Azure Kubernetes Service, DevOps, microservices, CI/CD, cloud-native computingAbstract
Containerization has become the dominant packaging model for modern cloud-native applications, and Kubernetes has emerged as the de facto standard for orchestrating containers at scale. Microsoft Azure Kubernetes Service (AKS) extends this orchestration capability as a managed platform, removing much of the operational burden associated with running a self-managed Kubernetes control plane while retaining full compatibility with the Kubernetes application programming interface and ecosystem. This article examines how containerized applications can be reliably built, deployed, scaled, and monitored on AKS, situating the discussion within the broader evolution of container orchestration research from early cluster schedulers such as Google Borg, Apache Mesos, and Omega through to Kubernetes itself. The article traces the technical lineage of container orchestration, compares AKS against Azure Container Instances and self-managed Kubernetes deployments, and proposes an end-to-end continuous integration and continuous delivery (CI/CD) pipeline architecture spanning source control, automated build and test, container image management through Azure Container Registry, cluster deployment, and post-deployment observability. Two tables and one pipeline architecture figure illustrate the comparative platform analysis and the mapping between common deployment challenges and AKS-specific mitigations. The findings indicate that AKS meaningfully reduces the operational overhead documented in the container orchestration literature, particularly around control-plane management, security patching, and autoscaling, while requiring organizations to still invest deliberately in image security scanning, deployment automation discipline, and cluster observability to realize these benefits in production. The article concludes with practical recommendations for teams adopting AKS for containerized application deployment.
References
Abdollahi V, L., Saied, M. A., Toeroe, M., & Khendek, F. (2018). Deploying microservice based applications with Kubernetes: Experiments and lessons learned. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 970–973). IEEE. https://doi.org/10.1109/CLOUD.2018.00148
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2017). Autonomic vertical elasticity of Docker containers with ElasticDocker. In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD) (pp. 472–479). IEEE. https://doi.org/10.1109/CLOUD.2017.67
Amaral, M., Polo, J., Carrera, D., Mohomed, I., Unuvar, M., & Steinder, M. (2015). Performance evaluation of microservices architectures using containers. In 2015 IEEE 14th International Symposium on Network Computing and Applications (NCA) (pp. 27–34). IEEE. https://doi.org/10.1109/NCA.2015.49
Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016). Microservices architecture enables DevOps: Migration to a cloud-native architecture. IEEE Software, 33(3), 42–52. https://doi.org/10.1109/MS.2016.64
Bernstein, D. (2014). Containers and cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing, 1(3), 81–84. https://doi.org/10.1109/MCC.2014.51
Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57. https://doi.org/10.1145/2890784
Casalicchio, E. (2019). Container orchestration: A survey. In A. Puliafito & K. S. Trivedi (Eds.), Systems Modeling: Methodologies and Tools (pp. 221–235). Springer. https://doi.org/10.1007/978-3-319-92378-9_14
Casalicchio, E., & Iannucci, S. (2020). The state-of-the-art in container technologies: Application, orchestration and security. Concurrency and Computation: Practice and Experience, 32(17), e5668. https://doi.org/10.1002/cpe.5668
Casalicchio, E., & Perciballi, V. (2017). Auto-scaling of containers: The impact of relative and absolute metrics. In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W) (pp. 207–214). IEEE. https://doi.org/10.1109/FAS-W.2017.149
Combe, T., Martin, A., & Di Pietro, R. (2016). To Docker or not to Docker: A security perspective. IEEE Cloud Computing, 3(5), 54–62. https://doi.org/10.1109/MCC.2016.100
Felter, W., Ferreira, A., Rajamony, R., & Rubio, J. (2015). An updated performance comparison of virtual machines and Linux containers. In 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (pp. 171–172). IEEE. https://doi.org/10.1109/ISPASS.2015.7095802
Hindman, B., Konwinski, A., Zaharia, M., Ghodsi, A., Joseph, A. D., Katz, R. H., Shenker, S., & Stoica, I. (2011). Mesos: A platform for fine-grained resource sharing in the data center. In Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (pp. 295–308). USENIX Association.
Kaiser, S., Haq, M. S., Tosun, A., & Korkmaz, T. (2022). Container technologies for ARM architecture: A comprehensive survey of the state-of-the-art. IEEE Access, 10, 84853–84881. https://doi.org/10.1109/ACCESS.2022.3197126
Kang, H., Le, M., & Tao, S. (2016). Container and microservice driven design for cloud infrastructure DevOps. In 2016 IEEE International Conference on Cloud Engineering (IC2E) (pp. 202–211). IEEE. https://doi.org/10.1109/IC2E.2016.26
Kozhirbayev, Z., & Sinnott, R. O. (2017). A performance comparison of container-based technologies for the cloud. Future Generation Computer Systems, 68, 175–182. https://doi.org/10.1016/j.future.2016.08.025
Kratzke, N., & Quint, P. C. (2017). Understanding cloud-native applications after 10 years of cloud computing: A systematic mapping study. Journal of Systems and Software, 126, 1–16. https://doi.org/10.1016/j.jss.2017.01.001
Medel, V., Rana, O., Bañares, J. Á., & Arronategui, U. (2016). Modelling performance & resource management in Kubernetes. In 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) (pp. 257–262). IEEE/ACM. https://doi.org/10.1145/2996890.3007869
Naha, R. K., Garg, S., Georgakopoulos, D., Jayaraman, P. P., Gao, L., Xiang, Y., & Ranjan, R. (2018). Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access, 6, 47980–48009. https://doi.org/10.1109/ACCESS.2018.2866491
Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2019). Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing, 7(3), 677–692. https://doi.org/10.1109/TCC.2017.2702586
Peinl, R., Holzschuher, F., & Pfitzer, F. (2016). Docker cluster management for the cloud: Survey results and own solution. Journal of Grid Computing, 14(2), 265–282. https://doi.org/10.1007/s10723-016-9366-y
Rodriguez, M. A., & Buyya, R. (2019). Container-based cluster orchestration systems: A taxonomy and future directions. Software: Practice and Experience, 49(5), 698–719. https://doi.org/10.1002/spe.2660
Schwarzkopf, M., Konwinski, A., Abd-El-Malek, M., & Wilkes, J. (2013). Omega: Flexible, scalable schedulers for large compute clusters. In Proceedings of the 8th ACM European Conference on Computer Systems (EuroSys) (pp. 351–364). ACM. https://doi.org/10.1145/2465351.2465386
Sultan, S., Ahmad, I., & Dimitriou, T. (2019). Container security: Issues, challenges, and the road ahead. IEEE Access, 7, 52976–52996. https://doi.org/10.1109/ACCESS.2019.2911732
Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., Saha, B., Curino, C., O'Malley, O., Radia, S., Reed, B., & Baldeschwieler, E. (2013). Apache Hadoop YARN: Yet another resource negotiator. In Proceedings of the 4th Annual Symposium on Cloud Computing (SoCC) (Article 5). ACM. https://doi.org/10.1145/2523616.2523633
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015). Large-scale cluster management at Google with Borg. In Proceedings of the 10th European Conference on Computer Systems (EuroSys) (Article 18). ACM. https://doi.org/10.1145/2741948.2741964
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