Integrated Monitoring for On-site storage Kubernetes Systems and Hybrid EKS
Keywords:
Hybrid Kubernetes clusters, EKS, observabilityAbstract
Managing Kubernetes systems across hybrid environments, such as Amazon Elastic Kubernetes Service & on-premises infrastructures, presents special problems as more businesses embrace containerized workloads. To guarantee seamless operations & reduces the downtime, workloads dispersed over multiple environments need a single monitoring system. Using different monitoring programs for on-premises & cloud systems might result in inefficiencies, delayed problem solving's & the poor visibilities. The significance of a single monitoring strategy that offers smooth observability across cloud & the on-premises Kubernetes systems is examined in this paper. Proactive management requires consolidated measurements, actual time monitoring & a comprehensive understanding of systems health. Teams can be more efficiently monitor apps while pick out problems before they become very serious & react fast to performance snags by combining cloud-native technologies with on-premises solutions. Additionally, a unified approach guarantees a consistent users experience, facilitates the efficient infrastructure scaling & optimizes the resources allocation. To provide a comprehensive picture of the system health of the infrastructure, strategies include metrics aggregation, distributed tracing & centralized logging. While we avoiding vendor lock-in, open-source solutions like as Prometheus, Grafana & OpenTelemetry allow for cross-environment monitoring integration. Aligning monitoring procedures, automating fixes for frequent problems & the guaranteeing that teams have access to useful data are examples of best practices. With the aid of centralized observability, this handbook assists the enterprises in reducing their downtime, increasing dependability & navigating the challenges of hybrid Kubernetes settings.
References
1. Choudhary, S. (2021). Kubernetes-Based Architecture For An On-premises Machine Learning Platform (Master's thesis).
2. Sabir, A., & Shahid, A. (2023). Effective Management of Hybrid Workloads in Public and Private Cloud Platforms (Master's thesis, uis).
3. Cannarella, A. (2022). Multi-Tenant federated approach to resources brokering between Kubernetes clusters (Doctoral dissertation, Politecnico di Torino).
4.Piscaer, J. (2019). Kubernetes in the enterprise. Bluffton: ActualTech Media.
5. Arundel, J., & Domingus, J. (2019). Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O'Reilly Media.
6. 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.
7. 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.
8. Limbrunner, N. (2023). Dynamic macro to micro scale calculation of energy consumption in CI/CD pipelines.
9. Basig, L., & Lazzaretti, F. (2021). Reliable Messaging Using the CloudEvents Router (Doctoral dissertation, OST Ostschweizer Fachhochschule).
10. Sluga, M. (2020). AWS Certified Developer-Associate (DVA-C01) Cert Guide. Pearson IT Certification.
11. Mehtonen, V. (2019). Research on building containerized web backend applications from a point of view of a sample application for a medium sized business.
12. Podolskiy, V. (2021). Predictive Autoscaling for Multilayered Cloud Deployments (Doctoral dissertation, Technische Universität München).
13. Gómez Escobar, J. A. (2019). Design of a reference architecture for an IoT sensor network.
14. 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.".
15. Mennuni, M. (2023). An Analysis of SOC Monitoring Systems (Doctoral dissertation, Politecnico di Torino).
16. Boda, V. V. R., & Immaneni, J. (2023). Automating Security in Healthcare: What Every IT Team Needs to Know. Innovative Computer Sciences Journal, 9(1).
17. Immaneni, J. (2023). Best Practices for Merging DevOps and MLOps in Fintech. MZ Computing Journal, 4(2).
18. Nookala, G. (2024). The Role of SSL/TLS in Securing API Communications: Strategies for Effective Implementation. Journal of Computing and Information Technology, 4(1).
19. Nookala, G. (2024). Adaptive Data Governance Frameworks for Data-Driven Digital Transformations. Journal of Computational Innovation, 4(1).
20. Komandla, V. Crafting a Clear Path: Utilizing Tools and Software for Effective Roadmap Visualization.
21. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
22. Thumburu, S. K. R. (2023). Data Quality Challenges and Solutions in EDI Migrations. Journal of Innovative Technologies, 6(1).
23. Thumburu, S. K. R. (2023). Mitigating Risk in EDI Projects: A Framework for Architects. Innovative Computer Sciences Journal, 9(1).
24. 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).
25. 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).
26. Gade, K. R. (2023). Event-Driven Data Modeling in Fintech: A Real-Time Approach. Journal of Computational Innovation, 3(1).
27. Katari, A., & Rodwal, A. NEXT-GENERATION ETL IN FINTECH: LEVERAGING AI AND ML FOR INTELLIGENT DATA TRANSFORMATION.
28. Katari, A. Case Studies of Data Mesh Adoption in Fintech: Lessons Learned-Present Case Studies of Financial Institutions.
29. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
30. Nookala, G. (2023). Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis. Journal of Computational Innovation, 3(1).
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
