Adaptive Reliability Engineering for Transaction-Intensive Enterprise Platforms
DOI:
https://doi.org/10.22399/ijcesen.5003Keywords:
Adaptive Reliability Engineering, Control Theory, Distributed Systems, Metastable Failures, Closed-Loop Systems, ObservabilityAbstract
Reliability engineering for transaction-intensive distributed platforms has evolved beyond static provisioning and threshold-based fault tolerance. Modern cloud-native systems operate under nonlinear workload volatility, metastable degradation risks, and complex dependency-induced failure propagation, rendering traditional reliability models insufficient.This paper formalizes Adaptive Reliability Engineering (ARE) as a control-theoretic framework that transforms reliability from static configuration into a closed-loop operational discipline. By integrating real-time telemetry, dynamic load shedding, health-aware routing, circuit breaker isolation, and feedback-driven resource governance, ARE enables continuous system stabilization under volatile demand conditions.The framework addresses metastable failure amplification, retry-induced cascading collapse, and inefficiencies in error-path execution by introducing adaptive control surfaces that dynamically regulate resource allocation and service-level objective (SLO) compliance.Unlike machine learning–dependent resource managers that require prolonged training cycles and exploration overhead, ARE emphasizes deterministic feedback control mechanisms capable of immediate responsiveness without extended data collection phases.The proposed framework generalizes across financial transaction infrastructures, digital commerce platforms, and cloud-native microservices architectures.
References
[1] Y. Zhang, et al., “Analytically-Driven Resource Management for Cloud-Native Microservices,”
in Proc. IEEE Int. Symp. High-Performance Computer Architecture (HPCA), 2024. doi: 10.48550/arXiv.2401.02920.
[2] N. Bronson, et al., “Metastable Failures in Distributed Systems,” in Proc. ACM Symp. Operating Systems Principles (SOSP), 2021. doi: 10.1145/3458336.3465286.
[3] P. J. Courtois, “Decomposability, Instabilities, and Saturation in Multiprogramming Systems,”
Communications of the ACM, vol. 18, no. 7, pp. 371–377, Jul. 1975. doi: 10.1145/360881.360887.
[4] J. Tavori, et al., “RetryGuard: Preventing Self-Inflicted Retry Storms in Cloud Microservices Applications,” arXiv preprint arXiv:2511.23278, 2025. doi: 10.48550/arXiv.2511.23278.
[5] Q. Fettes, et al., “Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices,” arXiv preprint arXiv:2304.07941, 2023. doi: 10.48550/arXiv.2304.07941.
[6] Z. Zhang, et al., “The Vision of Autonomic Computing: Can LLMs Make It a Reality?”
arXiv preprint arXiv:2407.14402, 2024. doi: 10.48550/arXiv.2407.14402.
[7] H. Jamjoom and K. G. Shin, “Persistent Dropping: An Efficient Control of Traffic Aggregates,”
in Proc. ACM SIGCOMM, 2003. doi: 10.1145/863955.863988.
[8] A. Wieder, et al., “Orchestrating the Deployment of Computations in the Cloud with Conductor,”
in Proc. USENIX Symp. on Networked Systems Design and Implementation (NSDI), 2012. [Available]: https://www.usenix.org/sites/default/files/conference/protected-files/conductor-slides-nsdi2012.pdf
[9] R. R. Sambasivan, et al., “Diagnosing Performance Changes by Comparing Request Flows,”
in Proc. USENIX Symp. on Networked Systems Design and Implementation (NSDI), 2011. [Available]: https://www.pdl.cmu.edu/PDL-FTP/SelfStar/NSDI11.pdf
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
