用零样本模型自动调优云服务器大小，省钱又省事。
CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift
- 结合零样本预测与多周期规划，生成可操作的实例配置建议。
- 实测月均成本从1503美元降至708美元，节省52.9%（约795美元）。
- 无需重新训练，适合运维压力大、负载波动的生产环境。
云虚拟机常被过度配置，导致不必要的成本和效率损失。本文提出CLOUDADV，一个面向工程师的交互式云实例大小调整建议系统，适用于工作负载漂移场景。该系统融合零样本时间序列预测，在日、周、月三个规划周期内生成受限推荐。每次查询基于历史利用率、预测摘要、当前虚拟机元数据、候选实例选项、定价信息及显式调优规则构建结构化决策上下文。使用大容量LLM离线生成参考建议，小模型在相同提示下评估部署时对齐度，以满足延迟与成本约束。评估聚焦下游建议质量，采用模拟Azure成本节约和事后超限率；滚动起点预测精度作为次要诊断，对比经典方法与监督基线。在七个生产级虚拟机的案例研究中，参考建议将模拟月成本从约1503美元降至708美元，实现每月795美元节省（52.9%），在降配案例中最高超限率为1.5%。尽管Chronos-2未在所有预测指标上最优，但其常产生与监督型按租户基线相似的推荐模式。结果表明，零样本基础模型可在非平稳云环境中支持决策对齐的资源分配，同时减少重复租户重训练、验证与部署的运维负担。
原文摘要 · Abstract (English)
Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency. We present CLOUDADV, an interactive engineer-facing advisory system for cloud instance sizing under workload drift. The system combines zero-shot time-series forecasting with bounded recommendation generation across day-, week-, and month-scale planning horizons. For each query, CLOUDADV constructs a structured decision context from historical utilization, forecast summaries, current VM metadata, candidate instance options, pricing, and explicit sizing heuristics. A higher-capacity LLM is used offline to generate reference recommendations, while a smaller production model is evaluated on the same prompts to assess deployment-time alignment under latency and cost constraints. Evaluation prioritizes downstream recommendation quality using simulated Azure cost savings and ex-post exceedance, with rolling-origin forecast accuracy reported as a secondary diagnostic against classical and supervised baselines. In a case study of seven production VMs, the reference recommendations reduce simulated monthly cost from about \$1,503 to \$708, yielding \$795/month in savings (52.9%) under conservative heuristic constraints, while the highest observed exceedance rate among downgraded cases is 1.5%. Although Chronos-2 does not minimize every forecasting metric, it often induces recommendation patterns similar to those of a supervised per-VM baseline. These results suggest that zero-shot foundation models can support decision-aligned provisioning in non-stationary cloud environments while reducing the operational burden of repeated per-tenant retraining, revalidation, and redeployment.
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