CloudCons Benchmark Reveals Foundation Models Excel at Forecasting but Not Cloud Resource Optimization
Researchers introduced CloudCons, a comprehensive benchmark for evaluating forecasting models in cloud resource consolidation using datasets from Huawei Cloud, Microsoft Azure, and Google Borg. While foundation models showed superior zero-shot forecasting accuracy, this advantage did not translate into better practical decision-making for resource optimization. The study highlights that selecting appropriate predictive quantiles is critical for balancing resource efficiency and service reliability in real-world cloud deployments.
CloudCons addresses a gap in cloud computing research by providing the first end-to-end benchmark specifically designed to evaluate forecasting models for cloud resource consolidation rather than just prediction accuracy. The benchmark uses high-quality datasets covering diverse workloads with distinct characteristics—from synchronized diurnal patterns to stochastic bursts and high-frequency noise—drawn from three major cloud providers. The researchers evaluated statistical models, deep learning approaches, and emerging foundation models, finding that while foundation models achieved superior zero-shot forecasting accuracy, this did not necessarily improve downstream decision utility for resource consolidation. A key practical finding is that the selection of predictive quantiles serves as a critical control lever for balancing the trade-off between resource efficiency and service reliability. The study provides actionable guidelines for practitioners deploying these models in production cloud environments.
What's missing
The paper does not discuss computational costs or inference latency of different model types, which could be relevant for real-time cloud resource consolidation decisions. Additionally, the study does not address how results might generalize to cloud providers beyond the three tested or to emerging workload patterns (e.g., AI/ML training jobs with different resource characteristics).
What different sources said
- arXiv cs.AICenter
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
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