Performance Analysis of Microservices Behavior in Cloud vs Containerized Domain based on CPU Utilization
Article Sidebar
Main Article Content
Abstract
Enterprise application development is rapidly moving towards a microservices-based approach. Microservices development makes application deployment more reliable and responsive based on their architecture and the way of deployment. Still, the performance of microservices is different in all environments based on resources provided by the respective cloud and services provided in the backend such as auto-scaling, load balancer, and multiple monitoring parameters. So, it is strenuous to identify Scaling and monitoring of microservice-based applications are quick as compared to monolithic applications [1]. In this paper, we deployed microservice applications in cloud and containerized environments to analyze their CPU utilization over multiple network input requests. Monolithic applications are tightly coupled while microservices applications are loosely coupled which help the API gateway to easily interact with each service module. With reference to monitoring parameters, CPU utilization is 23 percent in cloud environment. Additionally, we deployed the equivalent microservice in a containerized environment with extended resources to minimize CPU utilization to 17 percent. Furthermore, we have shown the performance of the application with “Network IN” and “Network Out” requests.
Article Details
References
Al-Doghman, F., Moustafa, N., Khalil, I., Tari, Z. and Zomaya, A., 2022. AI-enabled Secure Microservices in Edge Computing: Opportunities and Challenges. IEEE Transactions on Services Computing.
S. N. Srirama, M. Adhikari, and S. Paul, “Application deployment using containers with auto-scaling for microservices in cloud environment,” J. Netw. Comput. Appl., vol. 160, no. February, p. 102629, 2020, doi: 10.1016/j.jnca.2020.102629.
S. Chhabra and A. K. Singh, “A Probabilistic Model for Finding an Optimal Host Framework and Load Distribution in Cloud Environment,” Procedia Comput. Sci., vol. 125, pp. 683–690, 2018, doi: 10.1016/j.procs.2017.12.088.
Kithulwatta, W.M.C.J.T., Jayasena, K.P.N., Kumara, B.T. and Rathnayaka, R.M.K.T., 2022. Integration With Docker Container Technologies for Distributed and Microservices Applications: A State-of-the-Art Review. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 12(1), pp.1-22.
H. Xu and B. Li, “Dynamic Cloud Pricing for Revenue Maximization,” IEEE Trans. Cloud Comput., vol. 1, no. 2, pp. 158–171, 2013, doi: 10.1109/TCC.2013.15.
P. Jain, Y. Munjal, J. Gera, and P. Gupta, “Performance Analysis of Various Server Hosting Techniques,” Procedia Comput. Sci., vol. 173, no. 2019, pp. 70–77, 2020, doi: 10.1016/j.procs.2020.06.010.
Telang, T., 2023. Containerizing Microservices Using Kubernetes. In Beginning Cloud Native Development with MicroProfile, Jakarta EE, and Kubernetes (pp. 213-230). Apress, Berkeley, CA.1.
Bao, L., Wu, C., Bu, X., Ren, N. and Shen, M., 2019. Performance modeling and workflow scheduling of microservice-based applications in clouds. IEEE Transactions on Parallel and Distributed Systems, 30(9), pp.2114-2129.
Saman, B., 2017. Monitoring and analysis of microservices performance. Journal of Computer Science and Control Systems, 10(1), p.19..
Coulson, N.C., Sotiriadis, S. and Bessis, N., 2020. Adaptive microservice scaling for elastic applications. IEEE Internet of Things Journal, 7(5), pp.4195-4202..
Cerny, T., Donahoo, M.J. and Trnka, M., 2018. Contextual understanding of microservice architecture: current and future directions. ACM SIGAPP Applied Computing Review, 17(4), pp.29-45.
Adibatti, S. ., Sudhindra, K. R. ., & Manisha S., J. . (2023). 3 Phase Atrous Net with DCO-3DSPMRINET Model for Scoliosis Prediction. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 79–91. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2446.
Montesi, F. and Weber, J., 2016. Circuit breakers, discovery, and API gateways in microservices. arXiv preprint arXiv:1609.05830.
Wan, X., Guan, X., Wang, T., Bai, G. and Choi, B.Y., 2018. Application deployment using Microservice and Docker containers: Framework and optimization. Journal of Network and Computer Applications, 119, pp.97-109.
https://docs.aws.amazon.com/AWSEC2/latest UserGuide/viewing_metrics_with_cloudwatch.html
Manu, A.R., Patel, J.K., Akhtar, S., Agrawal, V.K. and Murthy, K.B.S., 2016, March. A study, analysis and deep dive on cloud PAAS security in terms of Docker container security. In 2016 international conference on circuit, power and computing technologies (ICCPCT) (pp. 1-13). IEEE.
Jaramillo, D., Nguyen, D.V. and Smart, R., 2016, March. Leveraging microservices architecture by using Docker technology. In SoutheastCon 2016 (pp. 1-5). IEEE..
Stubbs, J., Moreira, W. and Dooley, R., 2015, June. Distributed systems of microservices using docker and serfnode. In 2015 7th International Workshop on Science Gateways (pp. 34-39). IEEE.
Alam, M., Rufino, J., Ferreira, J., Ahmed, S.H., Shah, N. and Chen, Y., 2018. Orchestration of microservices for iot using docker and edge computing. IEEE Communications Magazine, 56(9), pp.118-123.
Singh, S. and Singh, N., 2016, July. Containers & Docker: Emerging roles & future of Cloud technology. In 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT) (pp. 804-807). IEEE.
Baresi, L., Quattrocchi, G. and Tamburri, D.A., 2022. Microservice Architecture Practices and Experience: a Focused Look on Docker Configuration Files. arXiv preprint arXiv:2212.03107.
Al-Debagy, O. and Martinek, P., 2018, November. A comparative review of microservices and monolithic architectures. In 2018 IEEE 18th International Symposium on Computational Intelligence and Informatics (CINTI) (pp. 000149-000154). IEEE.