Cloud Virtual Disk (CVD) placement in Cloud Block Storage (CBS) is critical for resource efficiency and performance isolation. Existing schemes prioritize spatial load balancing by dispersing disks across pods based on configuration-derived load estimates. However, overload risk in CBS is fundamentally temporal. Even when average load is balanced, pods can still suffer transient congestion when the peaks of co-located disks align in time. Achieving complementary placement, which co-locates CVDs with offset peaks, is hard at provisioning time because new disks have no history from which to infer temporal phase. We present TIDAL, a CVD placement framework that recovers phase-aware signals for cold-start placement from an underused source: tenant-provided names and identifiers in provisioning metadata. TIDAL first uses LLMs to recover application semantics from noisy metadata such as project, VM, and disk names. It then translates these semantics into phase-aware temporal signals to guide complementary placement. To satisfy control-plane constraints, TIDAL adopts an offline-to-online design with teacher-student distillation, regex-based filtering, and prefix-aware caching, enabling CPU-only inference with millisecond-level latency. Evaluations driven by production traces show that TIDAL reduces overload frequency by 79.1% and P95 overload duration by 73.7% compared with the strongest baselines.

翻译：云虚拟磁盘（CVD）在云块存储（CBS）中的部署对于资源效率和性能隔离至关重要。现有方案基于配置衍生负载估计，通过将磁盘分散到各节点来优先实现空间负载均衡。然而，CBS中的过载风险本质上是时间性的。即使平均负载保持均衡，当共置磁盘的峰值在时间上对齐时，各节点仍可能遭受瞬时拥塞。在供应阶段实现互补部署（即共置具有偏移峰值的CVD）十分困难，因为新磁盘缺乏推断时间相位的历史数据。我们提出TIDAL，一种从被忽略的数据源——供应元数据中租户提供的名称和标识符——中恢复冷启动部署相位感知信号的CVD部署框架。TIDAL首先利用LLM从项目、虚拟机及磁盘名称等噪声元数据中恢复应用语义，进而将这些语义转化为相位感知的时间信号以指导互补部署。为满足控制平面约束，TIDAL采用离线到在线设计框架，集成师生蒸馏、基于正则表达式的过滤及前缀感知缓存，实现毫秒级延迟的纯CPU推理。基于生产日志的评估表明，与最强基线相比，TIDAL将过载频率降低79.1%，P95过载持续时间降低73.7%。