TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics

TL;DR

TIDAL is a framework that uses large language models to infer temporal phases from metadata, enabling better cloud disk placement and significantly reducing overloads.

Contribution

It introduces a novel phase-aware placement method leveraging LLMs to improve cloud storage performance during cold-start scenarios.

Findings

Reduces overload frequency by 79.1%

Decreases P95 overload duration by 73.7%

Uses metadata and LLMs for phase inference in storage placement

Abstract

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.