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Abhijeet Das,
Digant Mondal,
Sahil Darmal,
Bhoomi Roy,
Shivam Kumar,
Rahul Verma,
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
- Student, Greater Noida Institute of Technology,Greater Noida,, Uttar Pradesh, India
Abstract
Kubernetes lets teams scale microservices and replace pods almost without notice. The same design that makes clusters reliable creates an intractable problem for API calls that require multiple steps on multiple individual services. A pod can stop suddenly. Any progress retained only in the memory vanishes. The client is forced to start the whole thing over. Data that is left behind can end up inconsistent. Durable state persistence is a way of changing the way that Kubernetes based microservices has handled. It transfers the record of what has already happened to storage that lasts longer than any one pod. The next one available reads the last record and continues from there. this review is how these techniques have evolved over the years. It begins with the early work on distributed transactions and works its way to the automated platforms people use today. The discussion is based on twenty sources dating back to 1987 to the early 2026. Checkpointing, event sourcing, the saga pattern, Temporal, Dapr Workflows and the improvements brought from speculative execution, all receive close attention. The primary interest is how these methods work with ordinary Kubernetes Deployments running ordinary standard HTTP or gRPC services. Durable execution engines when combined with good idempotency and clear tracing have turned out to be the most dependable choice in real clusters. History logs that are never-ending, steps that do not act as expected, and the inability to measure success in a consistent way still leave ample room for more work.
Keywords: Kubernetes microservices, durable execution, state persistence, workflow orchestration, fault tolerance, resilient API calls
References
[1] H. Garcia-Molina and K. Salem, “Sagas,” in Proc. ACM SIGMOD Int. Conf. Management of Data, San Francisco, CA, USA, May 1987, pp. 249–259. [2] Burckhardt S, Gillum C, Justo D, Kallas K, McMahon C, Meiklejohn CS. Durable functions: semantics for stateful serverless. Proceedings of the ACM on Programming Languages. 2021 Oct 15;5(OOPSLA):1-27. [3] Li T, Chandramouli B, Bernstein PA, Madden S. Distributed Speculative Execution for Resilient Cloud Applications. In20th USENIX Symposium on Operating Systems Design and Implementation (OSDI 26) 2026 (pp. 2027-2045). [4] Mohammad M. Resilient microservices: A systematic review of recovery patterns, strategies, and evaluation frameworks. In2025 5th International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA) 2025 Dec 17 (pp. 630-636). IEEE. [5] Chen Z, Goudarzi M, Toosi AN. Resilience evaluation of kubernetes in cloud- edge environments via failure injection. arXiv preprint arXiv:2507.16109. 2025 Jul 21. [6] Nadeem A, Malik MZ. A case for microservices orchestration using workflow engines. InProceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results 2022 May 21 (pp. 6-10). [7] ckittel. Design for Self-Healing – Azure Architecture Center. MicrosoftLearn. 2025. Available from: https://learn.microsoft.com/en- us/azure/architecture/guide/design-principles/self-healing [8] Cañete J. Durable Agent Execution with Temporal. Jacar. 2026. Available from: https://jacar.es/en/durable-agent-execution-with-temporal/ [9] Workflow architecture. Workflow architecture. Dapr Docs. 2026. Available from: https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow- architecture/ [10] Argo Workflows – The workflow engine for Kubernetes. Readthedocs.io. 2026. Available from: https://argo-workflows.readthedocs.io/en/release-4.1/ [11] Pattern: Transactional outbox. microservices.io. Chris Richardson; 2026. Available from: https://microservices.io/patterns/data/transactional-outbox.html [12] Ponce F, Gazza S, D’Iapico A, Amadini R, Brogi A, Forti S, Giallorenzo S, Plebani P, Usai D, Vitali M, Zavattaro G. Failure-resilient and carbon-efficient deployment of microservices over the cloud-edge continuum. arXiv preprint arXiv:2601.04123. 2026 Jan 7. [13] Soulé J, Jamont JP, Occello M, Traonouez LM, Théron P. Streamlining resilient kubernetes autoscaling with multi-agent systems via an automated online design framework. In2025 IEEE 18th International Conference on Cloud Computing (CLOUD) 2025 Jul 7 (pp. 43-53). IEEE. [14] Schmidt H, Rejiba Z, Eidenbenz R, Förster KT. Transparent fault tolerance for stateful applications in kubernetes with checkpoint/restore. In2023 42nd International Symposium on Reliable Distributed Systems (SRDS) 2023 Sep 25 (pp. 129-139). IEEE. [15] Sharbaf M, Zamani B, Sunyé G. CoMPers: A configurable conflict management framework for personalized collaborative modeling. Journal of Systems and Software. 2025 Jan 1;219:112227. [16] Díaz J, Quiñonez Y, De-la-Hoz-Franco E, Butt-Aziz S, Mercado T, Salcedo D. Information and communication technologies used in precision agriculture: A systematic review. AgriEngineering. 2025 Jun 2;7(6):167. [17] Burckhardt S, Gillum C, Justo D, Kallas K, McMahon C, Meiklejohn CS. Durable functions: semantics for stateful serverless. Proceedings of the ACM on Programming Languages. 2021 Oct 15;5(OOPSLA):1-27. [18] Gujarathi M. Event-driven architecture in long-running enterprise validation workflows. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM). 2025 Jul 10;8(4):12572-82. [19] Gaol FL, Fajar AN, Girsang AS, Matsuo T. Microservices Design Pattern in Action: Improving Modifiability in Microservices-Based Software Development. IEEE Access. 2025 Sep 16. [20] Su G, Liu L. Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification. Future Internet. 2024 Sep 3;16(9):319.
International Journal of Data Structure Studies
| Volume | 04 | |
| 02 | ||
| Received | 24/06/2026 | |
| Accepted | 29/07/2026 | |
| Published | 17/10/2026 | |
| Publication Time | 115 Days |