Yvaelis: If you want a rule set that still feels transparent, start with a finite-state machine that watches load metrics and flips a small set of weight parameters. Let a lightweight reinforcement loop adjust those weights, but keep the state transitions explicit so you can audit the decisions. That way you get adaptation without turning the allocator into a black box.

Idle, when the queue is empty or under threshold, then if a spike is detected move to ScaleUp. ScaleUp, you add resources, then if the load falls below a high‑threshold go to Balanced, otherwise stay ScaleUp. Balanced, normal operation, if the load climbs past a mid‑threshold go to ScaleUp, if it drops below a low‑threshold go to ScaleDown. ScaleDown, you remove resources, then if the load stabilises at mid‑range go to Balanced, otherwise stay ScaleDown. Overloaded, when resources hit a hard limit and the queue exceeds capacity, trigger an alert and stay until resources are freed or a fail‑over activates. Underloaded, if the system stays Idle for too long you can move to Maintenance or shutdown mode. Maintenance, manual override, you can reset counters and then return to Idle. Log every transition with a timestamp and the metric that caused it so the audit trail is clear.