Handling Failures in distributed networks with Resilience Engineering in the container Orchestration
Keywords:
Resilience Engineering, Container Orchestration, Distributed Systems, Service AvailabilityAbstract
Designing systems that can foresee, manage, and recover from failures is the goal of resilience engineering in container orchestration, which guarantees dependable performance even under unforeseen circumstances. Managing these settings has become more difficult as distributed systems are used in current applications. For containerization applications, platforms such as Kubernetes aids in automates deployment, scaling & operations. But they are not impervious to difficulties such as unexpected load spikes, software flaws, hardware malfunctions or network issues. In order to address these issues, they resilience engineering employs redundancies, automates rollbacks, dynamic load balancing, self-healing procedures, & vulnerability identification. These tactics lessons are the effect of failures & reduces downtime. Early anomaly detections & an understanding of the impact of failures on the system depends on monitoring, logging & actual time analysis. Resilient systems are designed to deteriorate gradually rather than catastrophically. Building a culture where failure is accepted as a chance to grow & learn is a crucial component of resilience. In containers orchestration, preserving service quality & ensuring the quick recovery are equally as important as avoiding failures. Businesses may create fault tolerant systems, enhances customer happiness & ensure companies continuity while growing in more complex contexts by using these concepts.
References
1. Chinamanagonda, S. (2023). Focus on resilience engineering in cloud services. Academia Nexus Journal, 2(1).
2. Kommera, A. R. (2013). The Role of Distributed Systems in Cloud Computing:
Scalability, Efficiency, and Resilience. NeuroQuantology, 11(3), 507-516.
3. Casalicchio, E., & Iannucci, S. (2020). The state‐of‐the‐art in container technologies: Application, orchestration and security. Concurrency and Computation: Practice and Experience, 32(17), e5668.
4. Aguilera, X. M., Otero, C., Ridley, M., & Elliott, D. (2018, July). Managed containers: A framework for resilient containerized mission critical systems. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 946-949). IEEE.
5. Casalicchio, E. (2019). Container orchestration: A survey. Systems Modeling: Methodologies and Tools, 221-235.
6. Acharya, J. N., & Suthar, A. C. (2021, October). Docker container orchestration management: A review. In International Conference on Intelligent Vision and Computing (pp. 140-153). Cham: Springer International Publishing.
7. Amiri, Z., Heidari, A., Navimipour, N. J., & Unal, M. (2023). Resilient and dependability management in distributed environments: A systematic and comprehensive literature review. Cluster Computing, 26(2), 1565-1600.
8. Dobson, S., Hutchison, D., Mauthe, A., Schaeffer-Filho, A., Smith, P., & Sterbenz,
J. P. (2019). Self-organization and resilience for networked systems: Design
principles and open research issues. Proceedings of the IEEE, 107(4), 819-834.
9. Burns, B. (2018). Designing distributed systems: patterns and paradigms for scalable, reliable services. " O'Reilly Media, Inc.".
10. Olorunnife, K., Lee, K., & Kua, J. (2021). Automatic failure recovery for container-based iot edge applications. Electronics, 10(23), 3047.
11. Aldwyan, Y., & Sinnott, R. O. (2019). Latency-aware failover strategies for containerized web applications in distributed clouds. Future Generation Computer Systems, 101, 1081-1095.
12. Heorhiadi, V., Rajagopalan, S., Jamjoom, H., Reiter, M. K., & Sekar, V. (2016, June). Gremlin: Systematic resilience testing of microservices. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) (pp. 57-66). IEEE.
13. Hale, A., Guldenmund, F., & Goossens, L. (2017). Auditing resilience in risk control and safety management systems. In Resilience Engineering (pp. 289-314). CRC Press.
14. Alam, M., Rufino, J., Ferreira, J., Ahmed, S. H., Shah, N., & Chen, Y. (2018). Orchestration of microservices for iot using docker and edge computing. IEEE Communications Magazine, 56(9), 118-123.
15. Poltronieri, F., Tortonesi, M., & Stefanelli, C. (2022, April). A chaos engineering approach for improving the resiliency of it services configurations. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium (pp. 1-6). IEEE
16. Katari, A., & Rodwal, A. NEXT-GENERATION ETL IN FINTECH: LEVERAGING AI AND ML FOR INTELLIGENT DATA TRANSFORMATION.
17. Katari, A. Case Studies of Data Mesh Adoption in Fintech: Lessons Learned-Present Case Studies of Financial Institutions.
18. Katari, A. (2023). Security and Governance in Financial Data Lakes: Challenges and Solutions. Journal of Computational Innovation, 3(1).
19. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
20. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
21. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2024). Building Cross-Organizational Data Governance Models for Collaborative Analytics. MZ Computing Journal, 5(1). 2024/3/13
22. Nookala, G. (2024). The Role of SSL/TLS in Securing API Communications: Strategies for Effective Implementation. Journal of Computing and Information Technology, 4(1). 2024/2/13
23. Nookala, G. (2024). Adaptive Data Governance Frameworks for Data-Driven Digital Transformations. Journal of Computational Innovation, 4(1). 2024/2/13
24. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2023). Zero-Trust Security Frameworks: The Role of Data Encryption in Cloud Infrastructure. MZ Computing Journal, 4(1).
25. Boda, V. V. R., & Immaneni, J. (2023). Automating Security in Healthcare: What Every IT Team Needs to Know. Innovative Computer Sciences Journal, 9(1).
26. Immaneni, J. (2023). Best Practices for Merging DevOps and MLOps in Fintech. MZ Computing Journal, 4(2).
27. Immaneni, J. (2023). Scalable, Secure Cloud Migration with Kubernetes for Financial Applications. MZ Computing Journal, 4(1).
28. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
29. Thumburu, S. K. R. (2023). Leveraging AI for Predictive Maintenance in EDI Networks: A Case Study. Innovative Engineering Sciences Journal, 3(1).
30. Thumburu, S. K. R. (2023). AI-Driven EDI Mapping: A Proof of Concept. Innovative Engineering Sciences Journal, 3(1).
31. Thumburu, S. K. R. (2023). EDI and API Integration: A Case Study in Healthcare, Retail, and Automotive. Innovative Engineering Sciences Journal, 3(1).
32. Thumburu, S. K. R. (2023). Quality Assurance Methodologies in EDI Systems Development. Innovative Computer Sciences Journal, 9(1).
33. Thumburu, S. K. R. (2023). Data Quality Challenges and Solutions in EDI Migrations. Journal of Innovative Technologies, 6(1).
34. Komandla, V. Crafting a Clear Path: Utilizing Tools and Software for Effective Roadmap Visualization.
35. Komandla, V. (2023). Safeguarding Digital Finance: Advanced Cybersecurity Strategies for Protecting Customer Data in Fintech.
36. Komandla, Vineela. "Crafting a Vision-Driven Product Roadmap: Defining Goals and Objectives for Strategic Success." Available at SSRN 4983184 (2023).
37. Komandla, Vineela. "Critical Features and Functionalities of Secure Password Vaults for Fintech: An In-Depth Analysis of Encryption Standards, Access Controls, and Integration Capabilities." Access Controls, and Integration Capabilities (January 01, 2023) (2023).
38. Komandla, Vineela. "Crafting a Clear Path: Utilizing Tools and Software for Effective Roadmap Visualization." Global Research Review in Business and Economics [GRRBE] ISSN (Online) (2023): 2454-3217.
39. 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
40. 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
41. 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
42. Muneer Ahmed Salamkar, and Jayaram Immaneni. Data Governance: AI Applications in Ensuring Compliance and Data Quality Standards. Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, May 2024, pp. 158-83
43. Naresh Dulam, et al. “Foundation Models: The New AI Paradigm for Big Data Analytics ”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, Oct. 2023, pp. 639-64
44. Naresh Dulam, et al. “Generative AI for Data Augmentation in Machine Learning”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, Sept. 2023, pp. 665-88
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. Sarbaree Mishra, and Jeevan Manda. “Improving Real-Time Analytics through the Internet of Things and Data Processing at the Network Edge ”. Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, Apr. 2024, pp. 184-06
52. Sarbaree Mishra. “Cross Modal AI Model Training to Increase Scope and Build More Comprehensive and Robust Models. ”. Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, July 2024, pp. 258-80
53. 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
54. 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
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