Author(s): V Phani Kumar and Avula Meghanatha Reddy

Abstract: Distributed software now operates at a complexity where infrastructure-level remediation alone does not guarantee correct system behaviour. Container orchestration can restart pods and reschedule workloads, yet applications frequently degrade due to latent logic defects, configuration regressions, dependency instability, and data drift that are invisible to coarse health probes. The dominant practice attaches observability as an external stack and relies on dashboards, playbooks, and human intervention, introducing detection latency and divorcing telemetry from the semantics embodied in code. This article advances an alternative: an AI-infused resilience model that treats observability, anomaly detection, diagnosis, and self-healing as intrinsic properties of the application layer and of the multi-application environment in which it executes. Evaluation relies on traffic replays, controlled fault injection, RCA benchmarks, and explicit cost accounting for instrumentation overhead. The overall result reframes “monitor and react” as “observe, infer, and repair,” complementing infrastructure self-healing with application-level behavioral correctness and system-level coordination. The approach is technically feasible with contemporary methods; adoption depends on disciplined instrumentation, trustworthy guardrails against false remediation, and progressive automation to build operator confidence. With advances in Operational Machine learning for AIOps, Deep learning for operational telemetry, empirical studies of distributed tracing for diagnosis in microservices with non-trivial overhead, and emergence of open datasets all have begun to standardize, enabling reproducible progress in localizing faults. Building on these foundations, the paper specifies a three-layer architecture for innate self-healing infrastructure.

DOI: 10.33545/27076571.2025.v6.i2b.188

Pages: 129-132 | Views: 43 | Downloads: 11

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