CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift

2026-06-30

AI Summary

This work addresses the inefficiencies and cost waste in cloud virtual machines caused by over-provisioning, particularly under shifting workloads that hinder dynamic tuning. The authors propose an engineer-centric, interactive instance tuning system that, for the first time, integrates zero-shot time-series forecasting models—such as Chronos-2—into non-stationary cloud environments. By leveraging large language models to generate offline recommendations, structured decision contexts, and multi-timescale suggestions, the system aligns resource allocation decisions with latency and cost constraints without requiring per-tenant model training, thereby substantially reducing operational overhead. Evaluation on seven production VMs demonstrates a 52.9% average monthly cost reduction (saving \$795) with only a 1.5% resource violation rate, achieving recommendation quality comparable to supervised baselines.

Abstract

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.

Problem

• cloud instance sizing
• workload drift
• overprovisioning
• decision alignment
• zero-shot forecasting

Innovation

• zero-shot foundation models
• decision-aligned recommendation
• cloud instance sizing
• workload drift
• forecasting under non-stationarity