Unified Monitoring for Hybrid EKS and On-Premises Kubernetes Clusters
Keywords:
Hybrid Kubernetes clusters, EKSAbstract
As organizations increasingly adopt containerized workloads, managing Kubernetes clusters across hybrid environments, including Amazon Elastic Kubernetes Service (EKS) and on-premises infrastructures, presents unique challenges. With workloads distributed across these different environments, maintaining a consistent and unified monitoring approach becomes crucial to ensure operational efficiency and minimize downtime. A fragmented monitoring system that treats cloud and on-premises clusters separately can result in delayed responses to issues, poor visibility, and operational inefficiencies. This paper addresses these complexities by proposing a unified monitoring solution providing seamless observability and management across cloud and on-premises Kubernetes clusters. It highlights the critical need for real-time monitoring, accurate performance metrics, & centralized observability for a holistic view of system health. By integrating cloud-native tools with on-premises monitoring solutions, organizations can streamline their observability and ensure proactive management of workloads. This unified approach allows teams to track the performance of their applications, identify potential issues before they escalate, and respond quickly to performance bottlenecks, improving overall system reliability. Furthermore, combining insights from cloud and on-premises environments enables teams to optimize resource allocation, scale infrastructure more efficiently, and ensure a consistent user experience across platforms. Key strategies outlined in the paper include leveraging centralized logging systems, distributed tracing, & metrics aggregation to view the infrastructure’s health comprehensively. Open-source tools, such as Prometheus, Grafana, and OpenTelemetry, are also discussed to integrate monitoring across multiple environments while minimizing vendor lock-in. Best practices for setting up a unified monitoring system are presented, including the importance of aligning monitoring and alerting protocols, automating responses to typical issues, and ensuring that teams have the proper access to the necessary data for decision-making. The paper ultimately serves as a guide for organizations seeking to overcome the challenges of hybrid Kubernetes environments, offering insights into achieving real-time observability, optimizing performance, and reducing downtime through a unified, centralized monitoring approach.
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References
Choudhary, S. (2021). Kubernetes-Based Architecture For An On-premises Machine Learning Platform (Master's thesis).
Sabir, A., & Shahid, A. (2023). Effective Management of Hybrid Workloads in Public and Private Cloud Platforms (Master's thesis, uis).
Cannarella, A. (2022). Multi-Tenant federated approach to resources brokering between Kubernetes clusters (Doctoral dissertation, Politecnico di Torino).
Piscaer, J. (2019). Kubernetes in the enterprise. Bluffton: ActualTech Media.
Arundel, J., & Domingus, J. (2019). Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O'Reilly Media.
Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C. C., Khandelwal, A., Pu, Q., ... & Patterson, D. A. (2019). Cloud programming simplified: A berkeley view on serverless computing. arXiv preprint arXiv:1902.03383.
Sagar, G., & Syrovatskyi, V. (2022). Cloud: On Demand Computing Resources for Scale and Speed. In Technical Building Blocks: A Technology Reference for Real-world Product Development (pp. 53-104). Berkeley, CA: Apress.
Limbrunner, N. (2023). Dynamic macro to micro scale calculation of energy consumption in CI/CD pipelines.
Basig, L., & Lazzaretti, F. (2021). Reliable Messaging Using the CloudEvents Router (Doctoral dissertation, OST Ostschweizer Fachhochschule).
Sluga, M. (2020). AWS Certified Developer-Associate (DVA-C01) Cert Guide. Pearson IT Certification.
Mehtonen, V. (2019). Research on building containerized web backend applications from a point of view of a sample application for a medium sized business.
Podolskiy, V. (2021). Predictive Autoscaling for Multilayered Cloud Deployments (Doctoral dissertation, Technische Universität München).
Gómez Escobar, J. A. (2019). Design of a reference architecture for an IoT sensor network.
Gift, N., & Charlesworth, J. (2022). Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform. " O'Reilly Media, Inc.".
Mennuni, M. (2023). An Analysis of SOC Monitoring Systems (Doctoral dissertation, Politecnico di Torino).
Boda, V. V. R., & Immaneni, J. (2023). Automating Security in Healthcare: What Every IT Team Needs to Know. Innovative Computer Sciences Journal, 9(1).
Immaneni, J. (2023). Best Practices for Merging DevOps and MLOps in Fintech. MZ Computing Journal, 4(2).
Nookala, G. (2024). The Role of SSL/TLS in Securing API Communications: Strategies for Effective Implementation. Journal of Computing and Information Technology, 4(1).
Nookala, G. (2024). Adaptive Data Governance Frameworks for Data-Driven Digital Transformations. Journal of Computational Innovation, 4(1).
Komandla, V. Crafting a Clear Path: Utilizing Tools and Software for Effective Roadmap Visualization.
Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
Thumburu, S. K. R. (2023). Data Quality Challenges and Solutions in EDI Migrations. Journal of Innovative Technologies, 6(1).
Thumburu, S. K. R. (2023). Mitigating Risk in EDI Projects: A Framework for Architects. Innovative Computer Sciences Journal, 9(1).
Gade, K. R. (2024). Cost Optimization in the Cloud: A Practical Guide to ELT Integration and Data Migration Strategies. Journal of Computational Innovation, 4(1).
Gade, K. R. (2023). The Role of Data Modeling in Enhancing Data Quality and Security in Fintech Companies. Journal of Computing and Information Technology, 3(1).
Gade, K. R. (2023). Event-Driven Data Modeling in Fintech: A Real-Time Approach. Journal of Computational Innovation, 3(1).
Katari, A., & Rodwal, A. NEXT-GENERATION ETL IN FINTECH: LEVERAGING AI AND ML FOR INTELLIGENT DATA TRANSFORMATION.
Katari, A. Case Studies of Data Mesh Adoption in Fintech: Lessons Learned-Present Case Studies of Financial Institutions.
Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
Nookala, G. (2023). Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis. Journal of Computational Innovation, 3(1).
Muneer Ahmed Salamkar. Data Visualization: AI-Enhanced Visualization Tools to Better Interpret Complex Data Patterns. Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, Feb. 2024, pp. 204-26
Muneer Ahmed Salamkar. Real-Time Analytics: Implementing ML Algorithms to Analyze Data Streams in Real-Time. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, Sept. 2023, pp. 587-12
Muneer Ahmed Salamkar. Feature Engineering: Using AI Techniques for Automated Feature Extraction and Selection in Large Datasets. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Dec. 2023, pp. 1130-48
Naresh Dulam, et al. “GPT-4 and Beyond: The Role of Generative AI in Data Engineering”. Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, Feb. 2024, pp. 227-49
Naresh Dulam, and Karthik Allam. “Snowpark: Extending Snowflake’s Capabilities for Machine Learning”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 2, Oct. 2023, pp. 484-06
Naresh Dulam, and Jayaram Immaneni. “Kubernetes 1.27: Enhancements for Large-Scale AI Workloads ”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, July 2023, pp. 1149-71
Sarbaree Mishra. “The Lifelong Learner - Designing AI Models That Continuously Learn and Adapt to New Datasets”. Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, Feb. 2024, pp. 207-2
Sarbaree Mishra, and Jeevan Manda. “Building a Scalable Enterprise Scale Data Mesh With Apache Snowflake and Iceberg”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, June 2023, pp. 695-16
Sarbaree Mishra. “Scaling Rule Based Anomaly and Fraud Detection and Business Process Monitoring through Apache Flink”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, Mar. 2023, pp. 677-98
Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77