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Calcrivo

Storage Growth Forecast

Forecast future storage needs from historical growth rate and planning horizon.

Inputs

GB
GB
GB/day

Days Until Full

70.0days

Projected Full Date

Oct 19, 2026

Remaining Capacity

350.00GB

Urgency

Moderate — schedule capacity review

Step by step

  1. Values used

    Total Capacity = 1,000 GB; Currently Used = 650 GB; Daily Growth = 5 GB/day

  2. Days until full

    days_until_full = (total − used) / daily_growth

  3. Days Until Full

    = 70.0 days

  4. Projected Full Date

    = Oct 19, 2026

  5. Remaining Capacity

    = 350.00 GB

  6. Urgency

    = Moderate — schedule capacity review

How it works

Given a constant daily growth rate, the runway before a disk fills is simply the remaining free capacity divided by that daily growth — the same math behind Kubernetes and cloud-provider disk-full predictions. This linear forecast assumes growth stays roughly steady; sudden traffic spikes, log storms, or new workloads landing on the volume can shrink the runway far faster than the trend suggests, so this estimate should be revisited regularly rather than treated as a one-time calculation.

Formula

Days until full

days_until_full = (total − used) / daily_growth

T
total capacity
U
currently used capacity
g
daily growth rate

Frequently Asked Questions

How should I measure daily growth accurately?

Take used-space samples (df -h or equivalent) at the same time each day over at least a week, then average the day-over-day differences — a single day's sample can be skewed by one-off events like a large backup or temp file cleanup.

What should I do once I know the disk-full date?

Set a monitoring alert well before the projected date (e.g. at 80% capacity), and start capacity expansion, cleanup, or data archival work with enough lead time to complete before urgency escalates — waiting until the disk is nearly full risks application failures or an emergency, unplanned expansion.

Why might the actual full date differ from this forecast?

Growth rates are rarely perfectly linear — traffic patterns, seasonal spikes, new features generating more data, or scheduled cleanup jobs (log rotation, cache eviction) can all accelerate or decelerate the trend, so this figure is best used as an early-warning estimate rather than an exact prediction.

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