arXiv:2606.13513v1 Announce Type: new Abstract: Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-optimize paradigm has emerged to optimize consolidation by anticipating future demands. While emerging time series foundation models promise to enhance this paradigm through zero-shot generalization, existing benchmarks focus solely on prediction error metrics. The actual decision utility of these advanced models remains unverified, rendering their practical value for down
The proliferation of time series foundation models and their potential application to cloud resource management makes it critical to evaluate their practical value beyond prediction error metrics.
This benchmark directly addresses the efficiency and financial implications of cloud data centers by improving resource utilization, which is a major operational cost.
The focus for evaluating AI models in cloud resource consolidation shifts from mere predictive accuracy to actual decision utility and optimization benefits, potentially leading to more efficient cloud infrastructure.
- · Cloud providers
- · AI model developers
- · Data center operators
- · Inefficient cloud service providers
- · Legacy resource management systems
Improved resource utilization in cloud data centers will lead to reduced operational costs and increased profitability for cloud providers.
Enhanced efficiency could allow for more compute-intensive applications at a lower cost, spurring innovation in AI and other data-heavy industries.
As cloud infrastructure becomes more optimized and cost-effective, this could indirectly contribute to the feasibility and scaling of compute-intensive initiatives like advanced AI research and sovereign AI infrastructure.
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Read at arXiv cs.AI