Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure | IJCT Volume 12 – Issue 6 | IJCT-V12I6P76
International Journal of Computer Techniques
ISSN 2394-2231
Author
Pruthvi Raj Seknametla
Abstract
Incident management in cloud-native DevOps environments has become an increasingly asymmetric challenge: the volume, velocity, and multi-dimensionality of observability telemetry that modern distributed systems generate far exceed the capacity of human operators and static rule-based monitoring to convert into reliable, timely, and actionable incident intelligence. AIOps — the application of data-driven analytical techniques to IT operations — offers a systematic framework for bridging this gap through continuous anomaly detection, intelligent event correlation, and automated or assisted remediation. This paper introduces the Predictive Incident Intelligence (PII) framework, a context-aware AIOps architecture that operates across four functional stages — signal conditioning, behavioral modelling, causal inference, and response orchestration — and integrates directly with the delivery pipeline toolchains, SLO governance structures, and GitOps change management workflows that define modern DevOps practice. A longitudinal empirical evaluation spanning thirteen cloud-native engineering teams over eighteen months demonstrates that PII adoption reduces mean time to detect service degradation by 64 percent, reduces mean time to mitigate by 57 percent, and cuts actionable alert volume by 76 percent compared to threshold-based monitoring baselines. We also identify five structural integration challenges specific to high-velocity DevOps environments and characterize the conditions under which PII-generated remediation recommendations translate into measurable reliability gains.
Keywords
AIOps, predictive incident management, DevOps observability, SRE, telemetry correlation, anomaly detection, cloud-native operations
Conclusion
This paper presented the Predictive Incident Intelligence framework, a four-stage AIOps architecture for cloud-native DevOps environments, and reported the results of an eighteen-month empirical evaluation across thirteen engineering teams. The core empirical finding is unambiguous: structured AIOps integration when designed for native compatibility with DevOps toolchains rather than as an operational overlay delivers substantial and reproducible improvements in both incident detection speed and incident resolution efficiency. The 64 percent MTTD reduction, 57 percent MTTM reduction, and 76 percent actionable alert volume reduction reported here represent improvements materially larger than those achievable through incremental refinement of threshold-based monitoring, and they are sustained across the full eighteen-month observation window rather than representing a one-time step-change that degrades over time.
Beyond the headline metrics, the study makes three analytical contributions that advance the field. The characterization of five structural DevOps-specific integration challenges telemetry schema drift, ephemeral workload model continuity, change velocity recalibration scheduling, multi-tenant attribution complexity, and automated remediation governance provides a practical risk register for adoption planning that the existing AIOps literature has not previously offered at this level of specificity. The maturity cohort analysis demonstrates that the adoption trajectory matters as much as the adoption decision: teams that invest in automated action governance concurrently with technical deployment achieve substantially better MTTM outcomes than those that defer governance to the post-deployment phase. And the emergent FinOps benefit 7.2 percent average compute cost reduction from improved emergency scaling and waste identification provides an additional economic justification for AIOps investment that finance and engineering leadership can evaluate alongside the operational improvements.
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How to Cite This Paper
Pruthvi Raj Seknametla (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
