Russian Federation
The article presents the development and analysis of a stochastic model of computational uncertainty in heterogeneous cloud platforms of 2026 using vectorized Monte Carlo. The authors investigate sources of uncertainty, theoretical foundations of modeling and integration with container orchestrators. The uniqueness of the work lies in the high accuracy of tail latency prediction, real-time simulation and practical effect: 25% increase in throughput, twofold reduction in SLA violations, 65–75% reduction in resource overprovisioning. The article is relevant because in modern clouds, stochastic delays critically affect SLA for AI and big data tasks, while traditional methods fail to handle extreme quantiles.
computational uncertainty, Monte Carlo, tail latency, cloud platforms, container orchestrators
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