研究人员推出CloudCons，一个旨在评估预测模型在云资源整合中有效性的新基准测试。现有基准测试主要关注预测准确性，忽略了模型在实际场景中的决策实用性。CloudCons使用来自Huawei Cloud、Azure和Google Borg的多样化数据集来评估各种统计模型、深度学习模型和基础模型。一项关键发现是，虽然基础模型在零样本预测准确性方面表现出色，但这并不能保证在资源整合方面的决策效用得到改善。 AI
排序理由 这是一篇介绍用于评估特定领域AI模型的新基准测试的研究论文。
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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…
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 …