Dynamic Resource Allocation in Kubernetes: Optimizing Cost and Performance
DOI:
https://doi.org/10.53555/ephijse.v7i3.237Keywords:
Kubernetes, dynamic resource allocation, cost optimization, performance optimization, vertical pod autoscaler (VPA)vertical pod autoscaler (VPA)Abstract
Dynamic resource allocation in Kubernetes is a critical aspect of modern cloud-native environments, where the primary goals are to optimize both cost and performance. As organizations increasingly adopt Kubernetes for its scalability, flexibility, and robust ecosystem, the need to efficiently manage resources becomes paramount. This paper delves into various strategies and techniques for dynamic resource allocation within Kubernetes clusters, aiming to achieve a balanced approach to cost-effectiveness and performance optimization. We explore the native capabilities of Kubernetes for resource management, including horizontal and vertical pod autoscaling, resource quotas, and limit ranges. Additionally, we examine advanced scheduling techniques and custom resource definitions that allow for fine-grained control over resource distribution. The paper highlights the role of metrics and monitoring tools, such as Prometheus and Grafana, in providing real-time insights into resource utilization and identifying optimization opportunities. Furthermore, we discuss the implementation of predictive analytics and machine learning models to anticipate workload demands and proactively adjust resources. Case studies from various industries illustrate the practical applications of these techniques, demonstrating significant improvements in both cost savings and system performance. By leveraging a combination of Kubernetes-native features, third-party tools, and advanced analytics, organizations can achieve a dynamic and responsive resource allocation strategy. This not only enhances the efficiency of Kubernetes clusters but also aligns with broader business objectives, such as reducing operational costs and maintaining high availability and performance standards. The insights presented in this paper provide a comprehensive guide for Kubernetes administrators and DevOps practitioners seeking to optimize their resource allocation strategies in a dynamic and cost-effective manner.
References
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.
Shelar, P. L. (2019). Dynamic Resources allocation using Priority Aware scheduling in Kubernetes (Doctoral dissertation, Dublin, National College of Ireland).
Li, D., Wei, Y., & Zeng, B. (2020, June). A dynamic I/O sensing scheduling scheme in Kubernetes. In Proceedings of the 2020 4th International Conference on High Performance Compilation, Computing and Communications (pp. 14-19).
Ferreira, A. P., & Sinnott, R. (2019, December). A performance evaluation of containers running on managed kubernetes services. In 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 199-208). IEEE.
Hu, Y., Zhou, H., de Laat, C., & Zhao, Z. (2020). Concurrent container scheduling on heterogeneous clusters with multi-resource constraints. Future Generation Computer Systems, 102, 562-573.
Pan, Y., Chen, I., Brasileiro, F., Jayaputera, G., & Sinnott, R. (2019, November). A performance comparison of cloud-based container orchestration tools. In 2019 IEEE International Conference on Big Knowledge (ICBK) (pp. 191-198). IEEE.
Goethals, T., De Turck, F., & Volckaert, B. (2020). Extending kubernetes clusters to low-resource edge devices using virtual kubelets. IEEE Transactions on Cloud Computing, 10(4), 2623-2636.
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.
Guerrero, C., Lera, I., & Juiz, C. (2018). Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. Journal of Grid Computing, 16, 113-135.
Nguyen, N., & Kim, T. (2020). Toward highly scalable load balancing in kubernetes clusters. IEEE Communications Magazine, 58(7), 78-83.
Chung, A., Park, J. W., & Ganger, G. R. (2018, October). Stratus: Cost-aware container scheduling in the public cloud. In Proceedings of the ACM symposium on cloud computing (pp. 121-134).
Rodriguez, M. A., & Buyya, R. (2019). Container‐based cluster orchestration systems: A taxonomy and future directions. Software: Practice and Experience, 49(5), 698-719.
Medel, V., Rana, O., Bañares, J. Á., & Arronategui, U. (2016, December). Modelling performance & resource management in kubernetes. In Proceedings of the 9th International Conference on Utility and Cloud Computing (pp. 257-262).
Yeh, T. A., Chen, H. H., & Chou, J. (2020, June). KubeShare: A framework to manage GPUs as first-class and shared resources in container cloud. In Proceedings of the 29th international symposium on high-performance parallel and distributed computing (pp. 173-184).
Gunasekaran, J. R., Thinakaran, P., Nachiappan, N. C., Kandemir, M. T., & Das, C. R. (2020, December). Fifer: Tackling resource underutilization in the serverless era. In Proceedings of the 21st International Middleware Conference (pp. 280-295).
