arXiv Machine Learning By Syed Izhan Khilji, Alireza Furutanpey, Schahram Dustdar

Incentives and Evidence in Learned Service Orchestration

Read the original on arXiv Machine Learning →

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.

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arXiv AI
Jun 11

When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

arXiv:2605. 26418v2 Announce Type: replace-cross Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help?

By Guilin Zhang, Chuanyi Sun, Kai Zhao, Shahryar Sarkani, John Fossaceca
arXiv Machine Learning
Sep 22

When Does Learning Beat Heuristics? A Case Study in Kubernetes Scheduler Score Plugins

The study investigates whether learned scoring functions can outperform hand‑tuned heuristics in Kubernetes node‑selection. Two models—a Random Forest on engineered features and a graph neural network on job dependency graphs—were trained on a large production trace; both achieved modest regression gains (R²≈0.042) but lagged behind a simple free‑CPU heuristic in Top‑1 ranking accuracy (65‑66% vs. 74‑84%). The authors attribute this gap to objective mismatch, noting that pointwise regression rather than a ranking‑specific loss likely limits performance.

By Wang Xuying, Zhibek Sarypbekova