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Cloud Storage Throttling

Cloud storage throttling slows or fails training when too many requests hit the storage service.

Quick answer

Cloud storage throttling slows or fails training when too many requests hit the storage service.

Infrastructure#cloud-storage#throttling#rate-limit#infrastructure#s3#gcs

What this failure is

Cloud Storage Throttling is a Infrastructure failure seen during ML training runs. Cloud storage throttling slows or fails training when too many requests hit the storage service. Common tags: Cloud Storage, Throttling, Rate Limit, Infrastructure.

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Why it happens (the mechanism)

Too many concurrent requests to storage. Storage service quota exceeded. Burst capacity exhausted. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • Training slows down over time
  • Checkpoint saves take progressively longer
  • Cloud storage API returns 429 (rate limit)

Common symptoms and what they mean

SymptomWhy it happens
S3: SlowDown errorToo many concurrent requests to storage
GCS: 429 rateLimitExceededStorage service quota exceeded
Azure: ServerBusyBurst capacity exhausted

Which systems are affected

  • Multi-node training with shared cloud storage
  • Frequent checkpointing to cloud storage
  • Large dataset training from cloud buckets

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • ✓Verified signal present: S3: SlowDown error
  • ✓Verified signal present: GCS: 429 rateLimitExceeded
  • ✓Verified signal present: Azure: ServerBusy
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Related failures to investigate next

Lambda Bgp Route Withdrawal Region Outage often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related primary cause next. lambda bgp route withdrawal region outage →
Kubernetes Gpu Pod Pending often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. kubernetes gpu pod pending →
Slurm Node Oom often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. slurm node oom →

Root cause

  • Too many concurrent requests to storage
  • Storage service quota exceeded
  • Burst capacity exhausted

The fix and how to prevent it

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References

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