Russian Federation
The article presents the development and analysis of a comprehensive stochastic model of computational uncertainty in heterogeneous cloud environments of 2026 based on convolution of component distributions. The authors decompose uncertainty into resource, network, orchestration and failure components, formalize total response time τ_total = Σ(τ_i + U_r,i) + ΣD_n,j + F_fail and implement vectorized Monte Carlo with FFT acceleration for millions of trajectories, Prometheus online calibration and multiphase modeling. The uniqueness of the work lies in p95-p99.9 tail latency prediction with <4% error, 20-40 sec lead autoscaling, 35% resource placement density increase and hidden correlation detection in ARM/x86/GPU clusters.
computational uncertainty, heterogeneous clouds, tail latency, Monte Carlo, Kubernetes orchestration
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