Counterfactual Telemetry Simulation for Release-Aware Capacity Guardrails and Performance Risk Mitigation in Microservice Architectures
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P111Keywords:
Counterfactual telemetry simulation, microservices, release-aware capacity guardrails, performance risk mitigation, Kubernetes, autoscaling, distributed tracing, observability, service-level objectives, canary deployment, cloud-native architectureAbstract
Modern microservice architectures enable rapid release velocity, elastic deployment, and fine-grained service ownership, yet they also introduce difficult performance-governance problems. A single release may alter service latency, resource demand, fan-out behavior, queue depth, retry amplification, downstream saturation, or autoscaling stability in ways that are not visible during conventional pre-production testing. Existing observability and autoscaling mechanisms remain largely reactive: they detect degradation after telemetry has already shifted, after error budgets have begun burning, or after horizontal scaling decisions have lagged behind workload growth. This paper proposes a counterfactual telemetry simulation framework for release-aware capacity guardrails and performance risk mitigation in microservice architectures. The framework combines distributed traces, metrics, logs, release metadata, workload features, dependency graphs, and capacity-state information to estimate what system behavior would likely look like under alternative release and capacity scenarios. Instead of asking only whether the current deployment is healthy, the proposed approach asks whether a new release would remain safe under plausible workload, dependency, and resource conditions. The paper develops a conceptual model, methodological pipeline, evaluation criteria, and analytical discussion for applying counterfactual simulation to release gates, canary expansion, autoscaling policy tuning, and service-level objective protection. The main contribution is a release-aware guardrail architecture that transforms telemetry from retrospective evidence into prospective operational intelligence. The proposed framework is particularly relevant for Kubernetes-based cloud-native systems where service versions, autoscaling thresholds, resource limits, and traffic splits evolve continuously. The paper argues that counterfactual telemetry simulation can reduce performance risk by improving pre-release capacity reasoning, detecting unsafe release-resource interactions, and supporting explainable mitigation actions before user-facing degradation occurs.
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