EKS Actual Observability Using Grafana as well as Prometheus
Keywords:
EKS, Kubernetes, Grafana, System MonitoringAbstract
Because of its scalability & flexibility, Kubernetes has been emerged as the preferred platform for orchestrating containerized applications. However, it is a harder to sustain performances & dependability in situations that are more complicated. Real-time observability is very necessary for continuous applications availability in order to efficiently monitor, assesses & fix the problems. Establishing a robust observability stack is more essential for organizations using Amazon Elastic Kubernetes Service. Metrics from Kubernetes components, such as nodes, pods & services which are gathered and stored by the open-source monitoring tool Prometheus. When used in conjunction with Grafana is a well-known visualization tool, it offers insightful data via dashboards that may be customized. When combined, they provide teams actual access to vital data like CPU, memory & the request latencies. Configuring Prometheus to scrape data from the clusters & combining it with Grafana for visualization is the process of deploying Prometheus as well as Grafana in Elastic Kubernetes Service. Grafana's dynamic dashboards simplifies complicated data, while the tool Prometheus alerts let teams know about the errors or abnormalities before they become more serious. In order to enable proactive monitoring & troubleshooting when the article explains how to set up a Prometheus as well as Grafana in an Elastic Kubernetes Service setup. Businesses can guarantee increases speed, dependability & high availability for their cloud-native apps with this solution.
References
1. Salecha, R. (2022). Observability. In Practical GitOps: Infrastructure Management Using Terraform, AWS, and GitHub Actions (pp. 449-503). Berkeley, CA: Apress.
2. Gleb, T., & Gleb, T. (2021). Add Monitoring, Logging and Alerting. Systematic Cloud Migration: A Hands-On Guide to Architecture, Design, and Technical Implementation, 111-138.
3. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
4. Henschel, J. (2021). Dimensioning, Performance and Optimization of Cloud-native Applications.
5. Raj, P., Vanga, S., & Chaudhary, A. (2022). Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications. John Wiley & Sons.
6. Gleb, T., & Gleb, T. (2021). Systematic Cloud Migration. Apress.
7. Camacho, C., Cañizares, P. C., Llana, L., & Núñez, A. (2022). Chaos as a Software Product Line—a platform for improving open hybrid‐cloud systems resiliency. Software: Practice and Experience, 52(7), 1581-1614.
8. Pinheiro, G. M. F. (2022). CI/CD Pipelines for Microservice-Based Architectures (Master's thesis, Universidade de Coimbra (Portugal)).
9. Chelliah, P. R., Naithani, S., & Singh, S. (2018). Practical Site Reliability Engineering: Automate the process of designing, developing, and delivering highly reliable apps and services with SRE. Packt Publishing Ltd.
10. Piscaer, J. (2019). Kubernetes in the enterprise. Bluffton: ActualTech Media.
11. Swaraj, N. (2022). Accelerating DevSecOps on AWS: Create secure CI/CD pipelines using Chaos and AIOps. Packt Publishing Ltd.
12. Söylemez, M., Tekinerdogan, B., & Kolukısa Tarhan, A. (2022). Feature-Driven Characterization of Microservice Architectures: A Survey of the State of the Practice. Applied Sciences, 12(9), 4424.
13. Tamiru, M. A. (2021). Automatic resource management in geo-distributed multi-cluster environments (Doctoral dissertation, Université de Rennes).
14. Rúa Martínez, J. D. L. (2020). Scalable architecture for automating machine learning model monitoring (Doctoral dissertation, ETSI_Informatica).
15. Abraha, A. W., Zerai, M. B., & Rihan, M. A. (2022). Kubernetes in VMware and NSX-T (Bachelor's thesis, NTNU).
16. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
17. Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
18. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
19. Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
20. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
21. Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
22. Thumburu, S. K. R. (2022). A Framework for Seamless EDI Migrations to the Cloud: Best Practices and Challenges. Innovative Engineering Sciences Journal, 2(1).
23. Thumburu, S. K. R. (2022). The Impact of Cloud Migration on EDI Costs and Performance. Innovative Engineering Sciences Journal, 2(1).
24. Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
25. Gade, K. R. (2022). Cloud-Native Architecture: Security Challenges and Best Practices in Cloud-Native Environments. Journal of Computing and Information Technology, 2(1).
26. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
27. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
28. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
29. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
30. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
31. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
32. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
33. 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
34. Naresh Dulam, et al. “Data Mesh Best Practices: Governance, Domains, and Data Products”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 524-47
35. Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42
36. Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77
37. Sarbaree Mishra. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, July 2022, pp. 329-52
38. Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28
39. Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03
40. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
41. Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10
