review of scheduler fairness in multi tenant inference

Current scheduler research suggests fairness-aware queueing policies can reduce tenant starvation while preserving acceptable utilization under mixed workloads (SIGCOMM publications).

see also: agent queue schedulers prioritize risk classes over arrival order · queue aware batching improves gpu utilization stability

evidence map

• Strict priority schemes often starve low-volume tenants.
• Weighted fairness improves predictability for shared clusters.
• Fairness controls can be tuned with modest throughput tradeoffs.

method boundary

Results depend on realistic tenant diversity and burst profiles in evaluation.

my take

Fairness is becoming a reliability property, not only a policy preference.

linkage

• [[agent queue schedulers prioritize risk classes over arrival order]]
• [[queue aware batching improves gpu utilization stability]]
• [[inference routing policies become board level controls]]

ending questions

which fairness metric best predicts tenant satisfaction in shared inference clusters?