ISSN : 2583-2646

Cost Optimization Strategies for Kubernetes Deployments in Cloud Environments

ESP Journal of Engineering & Technology Advancements
© 2021 by ESP JETA
Volume 1  Issue 1
Year of Publication : 2021
Authors : Anirudh Mustyala, Sumanth Tatineni
: 10.56472/25832646/ESP-V1I1P107

Citation:

Anirudh Mustyala, Sumanth Tatineni, 2021. "Cost Optimization Strategies for Kubernetes Deployments in Cloud Environments" ESP Journal of Engineering & Technology Advancements  1(1): 34-46.

Abstract:

In today's cloud-driven world, managing costs while maintaining robust and scalable Kubernetes deployments is a critical challenge for organizations. This article delves into practical and effective cost optimization strategies for Kubernetes deployments in cloud environments. We explore the use of spot instances, a cost-efficient option for running non-critical workloads at a fraction of the price of on-demand instances. By leveraging dynamic scaling, organizations can adjust resource allocation in real-time, ensuring they only pay for what they use without compromising performance. Additionally, setting appropriate resource requests and limits helps prevent over-provisioning and underutilization, both of which can drive up costs unnecessarily. Through real-world examples and actionable insights, we illustrate how these strategies can be implemented to achieve significant savings. Whether you're a small startup or a large enterprise, these techniques will empower you to optimize your Kubernetes deployments, making them both economical and efficient. Join us as we navigate the complexities of cost management in the cloud, offering a roadmap to achieving financial efficiency without sacrificing the power and flexibility of Kubernetes.

References:

[1] Zhong, Z., & Buyya, R. (2020). A cost-efficient container orchestration strategy in kubernetes-based cloud computing infrastructures with heterogeneous resources. ACM Transactions on Internet Technology (TOIT), 20(2), 1-24.
[2] Verreydt, S., Beni, E. H., Truyen, E., Lagaisse, B., & Joosen, W. (2019, December). Leveraging Kubernetes for adaptive and cost-efficient resource management. In Proceedings of the 5th International Workshop on Container Technologies and Container Clouds (pp. 37-42).
[3] Rossi, F., Cardellini, V., Presti, F. L., & Nardelli, M. (2020). Geo-distributed efficient deployment of containers with Kubernetes. Computer Communications, 159, 161-174.
[4] Rossi, F. (2020). Auto-scaling Policies to Adapt the Application Deployment in Kubernetes. In ZEUS (pp. 30-38).
[5] Zhao, P., Wang, P., Yang, X., & Lin, J. (2020). Towards cost-efficient edge intelligent computing with elastic deployment of container-based microservices. IEEE access, 8, 102947-102957.
[6] He, X., Tu, Z., Xu, X., & Wang, Z. (2019). Re-deploying microservices in edge and cloud environments for the optimization of user-perceived service quality. In Service-Oriented Computing: 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings 17 (pp. 555-560). Springer International Publishing.
[7] Tamiru, M. A., Tordsson, J., Elmroth, E., & Pierre, G. (2020, December). An experimental evaluation of the kubernetes cluster autoscaler in the cloud. In 2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 17-24). IEEE.
[8] Srirama, S. N., Adhikari, M., & Paul, S. (2020). Application deployment using containers with auto-scaling for microservices in cloud environment. Journal of Network and Computer Applications, 160, 102629.
[9] Rodriguez, M., & Buyya, R. (2020). Container orchestration with cost-efficient autoscaling in cloud computing environments. In Handbook of research on multimedia cyber security (pp. 190-213). IGI global.
[10] Buyya, R., Rodriguez, M. A., Toosi, A. N., & Park, J. (2018, November). Cost-efficient orchestration of containers in clouds: a vision, architectural elements, and future directions. In Journal of Physics: Conference Series (Vol. 1108, No. 1, p. 012001). IOP Publishing.
[11] Ungureanu, O. M., Vlădeanu, C., & Kooij, R. (2019, July). Kubernetes cluster optimization using hybrid shared-state scheduling framework. In Proceedings of the 3rd International Conference on Future Networks and Distributed Systems (pp. 1-12).
[12] Guerrero, C., Lera, I., & Juiz, C. (2018). Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications. The Journal of Supercomputing, 74(7), 2956-2983.
[13] James, A., & Schien, D. (2019, January). A Low Carbon Kubernetes Scheduler. In ICT4S.
[14] Kaminski, M., Truyen, E., Beni, E. H., Lagaisse, B., & Joosen, W. (2019, December). A framework for black-box SLO tuning of multi-tenant applications in Kubernetes. In Proceedings of the 5th International Workshop on Container Technologies and Container Clouds (pp. 7-12).
[15] Mao, Y., Fu, Y., Gu, S., Vhaduri, S., Cheng, L., & Liu, Q. (2020). Resource management schemes for cloud-native platforms with computing containers of docker and kubernetes. arXiv preprint arXiv:2010.10350.

Keywords:

Cost Optimization Strategies, Kubernetes, Cloud Environments.