Counterfactual Deployment Event Graphs for Explainable Cost Attribution and Resource Efficiency Optimization in Kubernetes Workloads
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I4P136Keywords:
Kubernetes, Counterfactual Explanation, Cloud Finops, Cost Attribution, Resource Efficiency, Autoscaling, Causal Inference, Deployment Event Graph, Observability, MicroservicesAbstract
Kubernetes has become the dominant substrate for cloud-native workload orchestration, yet its cost behavior remains difficult to explain because infrastructure expenditure is produced by a dynamic interaction among deployment events, scheduler decisions, autoscaling loops, resource requests, workload dependencies, and shared-node allocation policies. Existing cost observability systems often report expenditure retrospectively at namespace, service, or cluster levels, but they rarely explain why a cost increase occurred, which deployment event produced it, or what alternative configuration would have reduced waste without violating service-level objectives. This paper proposes Counterfactual Deployment Event Graphs (CDEGs), a conceptual and methodological framework for explainable cost attribution and resource efficiency optimization in Kubernetes workloads. CDEGs model Kubernetes operational history as a temporally indexed, causally annotated graph connecting deployments, pods, nodes, autoscalers, telemetry streams, billing records, configuration changes, and service dependencies. The framework integrates causal counterfactual reasoning, graph-based provenance, workload telemetry, and FinOps-oriented cost allocation to estimate how observed costs would have changed under alternative deployment decisions. Unlike purely correlational dashboards, CDEGs support path-specific cost explanations, actionable recourse recommendations, and guarded optimization policies for rightsizing, autoscaling, scheduling, and consolidation. The paper defines the problem, presents the graph model and counterfactual attribution procedure, describes a reference architecture, and proposes evaluation criteria for attribution fidelity, counterfactual validity, optimization effectiveness, operational overhead, and explanation usability. Analytical discussion demonstrates how CDEGs can distinguish legitimate elasticity from avoidable overprovisioning, identify deployment-induced waste, and bridge engineering and financial accountability. The study contributes a research agenda for explainable Kubernetes cost intelligence that is auditable, causally grounded, and practically aligned with production reliability constraints.
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