Developing Flexible Ingress Solutions on the EKS for High-Throughput Uses
Keywords:
Scalable ingress, Kubernetes, AWS ALB ingress controllerAbstract
For contemporary cloud-native apps to handle high-throughput systems, scalable ingress methods must be managed. As more businesses utilize Amazon EKS, effective traffic control is essential to meeting the needs of the growing user base. The methods for creating scalable ingress architectures that are specific to EKS are examined in this article. Important findings that provide flexibility, robust & their effective traffic routing managements are NGINX Ingress Controller & AWS ALB Ingress Controller. To effectively manage traffic spikes, best practices include using Kubernetes-native settings with optimizing resource allocations & integrating with AWS services like Auto Scaling & CloudFront. Secure traffic handlings is ensured by their security methods including AWS TLS termination & WAF integration. In addition to addressing practical issues like latency reduction, multi-tenant support & fulfilling their service-level agreements, the essay places a strong emphasis on observability & proactive monitoring. Developers & infrastructure architects can implement reliable ingress solutions that dynamically scale to ensure high availability & also performance under peak loads with the real-life applications & design considerations. With the help of this article, teams can successfully utilize Elastic Kubernetes Service (EKS) to offer outstanding user experiences while being scalable & resilient in the always changing Kubernetes environment.
References
1. Zhang, C., Zhang, S., Wang, Y., Li, W., Jin, B., Mok, R. K., ... & Xu, H. (2020, April). Scalable traffic engineering for higher throughput in heavily-loaded software defined networks. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-7). IEEE.
2. Aghdai, A., Xu, Y., & Chao, H. J. (2017, November). Design of a hybrid modular switch. In 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) (pp. 1-6). IEEE.
3. Clapp, K. P., Castan, A., & Lindskog, E. K. (2018). Upstream processing equipment. In Biopharmaceutical processing (pp. 457-476). Elsevier.
4. Wu, C. W., & Shen, C. A. (2018, November). The Hardware and Software Co-Design of a Stackable OpenFlow Switch for Software Defined Networking. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 858-862). IEEE.
5. Yu, T., Fayaz, S. K., Collins, M. P., Sekar, V., & Seshan, S. (2017, February). PSI: Precise Security Instrumentation for Enterprise Networks. In NDSS.
6. Stathakopoulou, C., David, T., Pavlovic, M., & Vukolić, M. (2019). Mir-bft: High-throughput robust bft for decentralized networks. arXiv preprint arXiv:1906.05552.
7. Zhang, Y., & Ansari, N. (2012). On architecture design, congestion notification, TCP incast and power consumption in data centers. IEEE Communications Surveys & Tutorials, 15(1), 39-64.
8. Subratie, K., Aditya, S., Daneshmand, V., Ichikawa, K., & Figueiredo, R. (2020). On the design and implementation of IP-over-P2P overlay virtual private networks. IEICE Transactions on Communications, 103(1), 2-10.
9. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
10. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
11. Salami, O., Chan, H. A., & Dlodlo, M. E. (2007, September). Non-priority QoS guarantee for next generation routers. In AFRICON 2007 (pp. 1-7). IEEE.
12. Reese, M. O., Dameron, A. A., & Kempe, M. D. (2011). Quantitative calcium resistivity based method for accurate and scalable water vapor transmission rate measurement. Review of Scientific Instruments, 82(8).
13. Jacobsen, M., Richmond, D., Hogains, M., & Kastner, R. (2015). RIFFA 2.1: A reusable integration framework for FPGA accelerators. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 8(4), 1-23.
14. LaCurts, K., Martins, M., Nagaraj, K., Ngo, K., & Scott, W. (2012). 9th USENIX Symposium on Networked Systems Design and Implementation 94.
15. Park, S., Al Maashri, A., Irick, K. M., Chandrashekhar, A., Cotter, M., Chandramoorthy, N., ... & Narayanan, V. (2012). System-on-chip for biologically inspired vision applications. IPSJ Transactions on System and LSI Design Methodology, 5, 71-95.
16. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
17. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
18. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
19. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
20. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
21. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
22. Thumburu, S. K. R. (2020). Exploring the Impact of JSON and XML on EDI Data Formats. Innovative Computer Sciences Journal, 6(1).
23. Thumburu, S. K. R. (2020). Large Scale Migrations: Lessons Learned from EDI Projects. Journal of Innovative Technologies, 3(1).
24. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
25. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
26. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
27. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
28. Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
29. Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
30. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
31. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
32. Naresh Dulam, et al. “Data As a Product: How Data Mesh Is Decentralizing Data Architectures”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
33. Naresh Dulam, et al. “Data Mesh in Practice: How Organizations Are Decentralizing Data Ownership ”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
34. Naresh Dulam. The Rise of Kubernetes: Managing Containers in Distributed Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 1, July 2015, pp. 73-94
35. Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
36. Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
37. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
38. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
