arXiv:2606.16555v1 Announce Type: cross Abstract: 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
This paper addresses a long-standing challenge in the practical application of reinforcement learning for service orchestration, which has been under research for over a decade but lacks widespread production deployment.
The findings challenge or confirm common assumptions about why AI-based orchestration systems fail in real-world environments, influencing future research and development in automated infrastructure management.
A clearer understanding of the limitations and vulnerabilities of current RL orchestration systems under realistic conditions could lead to more robust and deployable AI solutions for managing complex digital services.
- · Cloud infrastructure providers
- · AI/ML research community
- · Large-scale service operators
- · Developers of robust RL algorithms
- · Companies relying on immature RL orchestration
- · Providers of non-adaptive orchestration solutions
Further research and development will focus on making RL orchestration resilient to real-world challenges like noisy telemetry and workload shifts.
Increased adoption of more robust AI-driven orchestration tools, leading to more efficient and reliable large-scale service operations.
A competitive landscape where infrastructure automation is increasingly characterized by advanced, self-optimizing AI models.
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Read at arXiv cs.LG