CloudCons Benchmark Reveals Foundation Models Excel at Forecasting but Not Cloud Resource Optimization
Researchers have introduced CloudCons, an end-to-end benchmark for evaluating forecasting models in cloud resource consolidation, accepted to KDD 2026. The study tests statistical, deep learning, and foundation models on workload data from Huawei Cloud, Microsoft Azure, and Google Borg. Its key finding challenges a common assumption: superior zero-shot forecasting accuracy in foundation models does not automatically lead to better real-world resource management decisions.
CloudCons is a new benchmark designed to evaluate how well forecasting models perform within the practical context of cloud resource consolidation, addressing a gap in existing benchmarks that measure only prediction error rather than downstream decision utility. The research is motivated by persistently low resource utilization in cloud data centers, which stems from conservative over-provisioning intended to ensure service reliability. The benchmark incorporates high-quality datasets spanning diverse workloads from Huawei Cloud, Microsoft Azure, and Google Borg, covering patterns from synchronized diurnal rhythms to stochastic, pulse-like bursts and high-frequency noise. Extensive evaluation of statistical, deep learning, and foundation models reveals that while foundation models achieve superior zero-shot forecasting accuracy, this advantage does not inherently translate into better decision utility for consolidation tasks. The study also systematically examines how the selection of predictive quantiles serves as a critical lever for balancing resource efficiency against service reliability, offering actionable calibration guidelines for real-world deployment.
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- arXiv cs.AICenter
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
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