Vol. 2 No. 1 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

Advanced Networking Architectures for Modern Containerized Workloads

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  • Sandeep Chinamanagonda
Sandeep Chinamanagonda
Senior Software Engineer at Oracle Cloud infrastructure, USA
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Published 27-03-2022

Keywords

  • Container Networking,
  • Service Mesh
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sandeep Chinamanagonda, “Advanced Networking Architectures for Modern Containerized Workloads”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 1, pp. 180–205, Mar. 2022, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/239

Abstract

In the evolving landscape of cloud computing, containerization has become the standard for deploying modern workloads due to its flexibility, portability, and scalability. However, the rise of containerized workloads has introduced significant challenges for traditional networking architectures. This calls for advanced networking solutions that address containers' dynamic, ephemeral nature while ensuring security, reliability, and performance. Advanced networking architectures, such as service meshes, software-defined networking (SDN), and overlay networks, are crucial in orchestrating the communication needs of modern microservices-based applications. These solutions enable seamless inter-service communication, advanced traffic management, and policy-driven security without overwhelming infrastructure complexity. Additionally, they support multi-cluster and hybrid-cloud environments, facilitating greater agility and resilience in deployment strategies. Technologies like eBPF and Network Service Mesh (NSM) also redefine container networking by providing more efficient, programmable, and observable networking capabilities. As containers scale rapidly, these advanced architectures can mitigate traditional approaches' bottlenecks and security concerns, offering better load balancing, automated network configuration, and improved fault tolerance. Adopting these networking solutions helps organizations achieve more robust, responsive, and secure infrastructures essential for modern workloads. This abstract discusses the critical aspects of these advanced architectures and their role in addressing the complexity of networking for containerized applications. As the industry continues to innovate, understanding and implementing these networking solutions will be vital for staying competitive in a cloud-native world.

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References

  1. Watada, J., Roy, A., Kadikar, R., Pham, H., & Xu, B. (2019). Emerging trends, techniques and open issues of containerization: A review. IEEE Access, 7, 152443-152472.
  2. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
  3. Beltre, A. M., Saha, P., Govindaraju, M., Younge, A., & Grant, R. E. (2019, November). Enabling HPC workloads on cloud infrastructure using Kubernetes container orchestration mechanisms. In 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC) (pp. 11-20). IEEE.
  4. Hausenblas, M. (2018). Container Networking. O'Reilly Media, Incorporated.
  5. Pahl, C., Helmer, S., Miori, L., Sanin, J., & Lee, B. (2016, August). A container-based edge cloud paas architecture based on raspberry pi clusters. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) (pp. 117-124). IEEE.
  6. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
  7. Benomar, Z., Longo, F., Merlino, G., & Puliafito, A. (2020). Cloud-based enabling mechanisms for container deployment and migration at the network edge. ACM Transactions on Internet Technology (TOIT), 20(3), 1-28.
  8. Baranov, A. V., Savin, G. I., Shabanov, B. M., Shitik, A. S., Svadkovskiy, I. A., & Telegin, P. N. (2019). Methods of jobs containerization for supercomputer workload managers. Lobachevskii Journal of Mathematics, 40, 525-534.
  9. Hu, Y., Song, M., & Li, T. (2017, April). Towards" full containerization" in containerized network function virtualization. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 467-481).
  10. Jayalakshmi, S. (2020, October). Energy Efficient Next-Gen of Virtualization for Cloud-native Applications in Modern Data Centres. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 203-210). IEEE.
  11. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
  12. 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).
  13. Anderson, J. D. (2018). Improving Network Protocol Uptime During Upgrades Through Component Containerization (Master's thesis, College of Charleston).
  14. Vallee, G., Gutierrez, C. E. A., & Clerget, C. (2019, November). On-node resource manager for containerized HPC workloads. In 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC) (pp. 43-48). IEEE.
  15. 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).
  16. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
  17. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
  18. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
  19. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
  20. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
  21. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  22. Thumburu, S. K. R. (2021). The Future of EDI Standards in an API-Driven World. MZ Computing Journal, 2(2).
  23. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
  24. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
  25. Thumburu, S. K. R. (2021). Transitioning to Cloud-Based EDI: A Migration Framework, Journal of Innovative Technologies, 4(1).
  26. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  27. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
  28. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  29. Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018).
  30. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  31. Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
  32. 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
  33. 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
  34. Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93
  35. Naresh Dulam, et al. Apache Arrow: Optimizing Data Interchange in Big Data Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 93-114
  36. Naresh Dulam, and Venkataramana Gosukonda. Event-Driven Architectures With Apache Kafka and Kubernetes. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 115-36
  37. Naresh Dulam, et al. Snowflake Vs Redshift: Which Cloud Data Warehouse Is Right for You? . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Oct. 2018, pp. 221-40
  38. Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
  39. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37
  40. Sarbaree Mishra, and Jeevan Manda. “Incorporating Real-Time Data Pipelines Using Snowflake and Dbt”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Mar. 2021, pp. 205-2
  41. Sarbaree Mishra. “Building A Chatbot For The Enterprise Using Transformer Models And Self-Attention Mechanisms”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 1, May 2021, pp. 318-40
  42. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
  43. 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

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