Academic and research computing has a distinctive profile: long quiet periods, then a sudden need for substantial capacity before a submission deadline, all within a grant that does not grow.
Design for burst, not baseline
- Keep nothing large running between experiments.
- Script environment creation so capacity can be summoned and dismissed.
- Use interruptible capacity for anything that checkpoints.
- Store datasets in object storage, not on instance disks.
- Separate the interactive analysis environment from the batch compute.
Reproducibility is the research requirement
A result that cannot be reproduced is a problem in a way that a commercial deployment usually is not. That argues for the same discipline good engineering teams use, applied for a different reason.
- Pin every dependency version; record the container image digest.
- Version datasets, and record which version each run used.
- Keep the analysis code in version control, including notebooks.
- Log parameters and metrics for every run to a durable store.
- Describe the environment in code so it can be rebuilt years later.
Making a fixed budget last
| Leak | Fix |
|---|---|
| Idle notebook instances | Automatic shutdown after inactivity. |
| Oversized instances “to be safe” | Measure one run, then size. |
| Data transferred repeatedly | Cache locally within the region. |
| Storage of superseded results | Lifecycle rules with a review date. |
| Forgotten experiments | Tag with an owner and an expiry. |
Isolation per project
A separate account per research project keeps the grant accounting simple and prevents one student’s runaway job from consuming a colleague’s remaining budget. It also makes the eventual wind-down trivial.
Summary
Burst rather than idle, checkpoint so interruptible capacity is usable, pin everything for reproducibility, and isolate per project so the budget stays legible.
