Cloud Cost Monitoring Strategies for Large-Scale Amazon EKS Clusters
Keywords:
Amazon EKS, Multi-Tenant ClustersAbstract
Managing costs in cloud environments has become increasingly important as organizations scale their infrastructure. Keeping track of cloud spending can be a significant challenge for Kubernetes-based systems like Amazon EKS, where multiple services and workloads run across dynamic and complex clusters. This article proposes a robust cost-monitoring approach for large-scale EKS clusters, helping organizations optimize their cloud expenditure while maintaining efficient performance. The focus is leveraging key strategies, tools, and methodologies that enable real-time cost visibility and accountability across multiple tenants and workloads. It highlights the importance of integrating cost-tracking solutions with Kubernetes-native monitoring tools and practices, such as Prometheus and AWS Cost Explorer, to gather detailed insights into resource utilization and cost distribution. By adopting these strategies, enterprises can identify inefficiencies, reduce wastage, and better understand their cloud spending patterns. Ultimately, this article guides organizations seeking to implement a practical cost-monitoring framework, providing a clear, actionable solution for managing and optimizing cloud expenses in large, multi-tenant EKS environments.
Downloads
References
Sikeridis, D., Papapanagiotou, I., Rimal, B. P., & Devetsikiotis, M. (2017). A Comparative taxonomy and survey of public cloud infrastructure vendors. arXiv preprint arXiv:1710.01476.
Sayfan, G. (2018). Mastering Kubernetes: Master the art of container management by using the power of Kubernetes. Packt Publishing Ltd.
Arundel, J., & Domingus, J. (2019). Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O'Reilly Media.
Chen, G. (2019). Modernizing Applications with Containers in Public Cloud. Amazon Web Services.
Baier, J., & White, J. (2018). Getting Started with Kubernetes: Extend your containerization strategy by orchestrating and managing large-scale container deployments. Packt Publishing Ltd.
Menga, J. (2018). Docker on Amazon Web Services: Build, deploy, and manage your container applications at scale. Packt Publishing Ltd.
Raju, C. V. N. (2015). Data Integration with Spatial Data Mining and Security Model in Cloud Computing. International Journal of Advance Research in Computer Science and Management Studies, 3(11), 272-279.
Li, Z., Zhang, H., O’Brien, L., Cai, R., & Flint, S. (2013). On evaluating commercial cloud services: A systematic review. Journal of Systems and Software, 86(9), 2371-2393.
Martınez, P. J. C. (2011). A Middleware framework for selfadaptive large scale distributed services (Doctoral dissertation, PhD thesis, Universitat Politecnica de Catalunya, Departament d’Arquitectura dels Computadors, 2011.(Cited on pages 72, 78, 81, and 82.)).
Chacin Martínez, P. J. (2011). A Middleware framework for self-adaptive large scale distributed services.
Wunder, S. (2005). Payments for environmental services: some nuts and bolts (Vol. 42, pp. 1-32). Bogor: Cifor.
Krautheim, F. J. (2010). Building trust into utility cloud computing. University of Maryland, Baltimore County.
Duan, Y. C. (2014). Market research of commercial recommendation engines for online and offline retail (Doctoral dissertation, Massachusetts Institute of Technology).
Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
Myllylä, S. (2015). Terrains of struggle: the Finnish forest industry cluster and corporate community responsibility to Indigenous Peoples in Brazil (Doctoral dissertation, University of Jyväskylä).
Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
Naresh Dulam. The Shift to Cloud-Native Data Analytics: AWS, Azure, and Google Cloud Discussing the Growing Trend of Cloud-Native Big Data Processing Solutions. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Feb. 2015, pp. 28-48
Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.