Runtime Causal Fingerprinting for Root-Cause-Aware Autoscaling and Adaptive Resource Governance in Kubernetes Service Meshes
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P116Keywords:
Kubernetes, Service Mesh, Autoscaling, Causal Inference, Root-Cause Analysis, Distributed Tracing, Microservices, Adaptive Resource Governance, SLO-Aware Resource Management, Cloud-Native SystemsAbstract
Kubernetes-orchestrated microservice systems increasingly operate under volatile workload patterns, strict latency service-level objectives, and complex inter-service dependencies introduced by service mesh communication layers. Although Kubernetes provides established autoscaling mechanisms, conventional reactive autoscaling policies frequently interpret symptoms such as CPU saturation, request latency, or queue growth as direct scaling triggers without distinguishing whether the observed degradation is caused by workload demand, downstream dependency propagation, resource contention, noisy-neighbor effects, configuration drift, or release-induced regression. This limitation often leads to unnecessary scaling, delayed remediation, over-provisioning, and repeated service-level objective violations. This paper proposes a runtime causal fingerprinting framework for root-cause-aware autoscaling and adaptive resource governance in Kubernetes service meshes. The framework constructs continuously updated causal fingerprints from distributed traces, service mesh telemetry, container metrics, event streams, and workload signals. These fingerprints encode not only anomaly signatures but also causal propagation patterns across services, pods, nodes, and resource dimensions. The proposed approach integrates causal graph inference, temporal fingerprint matching, SLO-risk estimation, and policy-constrained scaling actions within a closed-loop governance architecture. Unlike conventional autoscalers that scale based primarily on local resource utilization, the proposed framework differentiates demand-driven saturation from causally propagated degradation and selects remediation strategies accordingly. The paper develops the conceptual model, methodological pipeline, evaluation criteria, and analytical discussion necessary for implementing root-cause-aware autoscaling in production-grade Kubernetes environments. The study contributes a technically grounded framework for improving autoscaling precision, reducing false-positive scaling, mitigating cascading failures, and strengthening operational trust in cloud-native platforms.
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