Incentives and Evidence in Learned Service Orchestration
Reinforcement learning for service orchestration has been the subject of sustained research for over a decade, yet it is not used in production at scale. The usual explanation is that learned controllers degrade under delayed and noisy telemetry, workload shifts, and uncontrolled tenants. We test whether existing evidence supports that explanation. We evaluate three highly influential RL-based orchestration systems spanning resource allocation, DAG scheduling, and autoscaling, using pre-registered predictions about comparative degradation under production-relevant perturbations and paired inference with family-wise error correction. Across the tests, most predicted performance reversals d...
| Authors | Syed Izhan Khilji, Alireza Furutanpey, Schahram Dustdar |
|---|---|
| Primary category | cs.DC |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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