Disaster recovery strategies (backup, pilot light, warm standby) in AWS - Time & Space Complexity
When planning disaster recovery in the cloud, it's important to know how the time to recover grows as your data or resources increase.
We want to understand how the recovery steps scale with the size of your infrastructure.
Analyze the time complexity of these disaster recovery strategies.
// Backup: Periodic snapshots of data
// Pilot Light: Minimal core system running
// Warm Standby: Scaled-down duplicate environment
// Recovery involves restoring data and starting services
This sequence shows the main steps in each strategy to recover after a failure.
Look at what actions happen repeatedly during recovery.
- Primary operation: Restoring data from backups or snapshots.
- How many times: Depends on the amount of data and number of services to restart.
As your data size and number of services grow, the recovery time grows roughly in proportion.
| Input Size (n) | Approx. Recovery Steps |
|---|---|
| 10 GB data, 2 services | Few restore and start operations |
| 100 GB data, 10 services | Many restore and start operations |
| 1 TB data, 50 services | Much more restore and start operations |
Pattern observation: Recovery time grows roughly linearly with data size and number of services.
Time Complexity: O(n)
This means recovery time grows in a straight line as your data and services increase.
[X] Wrong: "Recovery time stays the same no matter how much data or how many services I have."
[OK] Correct: More data and services mean more to restore and start, so recovery takes longer.
Understanding how recovery time grows helps you design better disaster plans and explain your choices clearly.
"What if we used a full warm standby environment instead of pilot light? How would the recovery time complexity change?"