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HELIOS: Guardrailed LLM-Driven Evolution of Autonomous Resource Orchestration Policies for Multi-Cloud Distributed Systems — arXiv:2609.09164

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.09164
License (as archived): CC0-1.0
Archived: 2026-10-16T07:54:01.098Z
SHA-256 of the archived text: 344f6c7d8587936c58da29cc1a6143de6933b0b1d90c80c16fb30c9f7feb08e8
Stable URL of this page: https://metraipolis.com/documento/dc_830fccb555ea

How to cite

HELIOS: Guardrailed LLM-Driven Evolution of Autonomous Resource Orchestration Policies for Multi-Cloud Distributed Systems — arXiv:2609.09164 — arXiv (en), https://arxiv.org/abs/2609.09164 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_830fccb555ea (Metraipolis archive dc_830fccb555ea, sha256 344f6c7d8587936c58da29cc1a6143de6933b0b1d90c80c16fb30c9f7feb08e8)

BibTeX

@misc{dc830fccb555ea,
  title = {HELIOS: Guardrailed LLM-Driven Evolution of Autonomous Resource Orchestration Policies for Multi-Cloud Distributed Systems — arXiv:2609.09164},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.09164},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_830fccb555ea; sha256 344f6c7d8587936c58da29cc1a6143de6933b0b1d90c80c16fb30c9f7feb08e8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.09164. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Operating latency-sensitive services across multiple public clouds creates an optimization surface no single provider autoscaler can see: on-demand vCPU prices differ by provider, spot discounts and interruption risks vary by provider and instance type, egress fees penalize state movement, and provider-level failures can take down single-cloud deployments. Large language models (LLMs) are appealing orchestrators because they can synthesize non-trivial decision logic from a natural-language environment description. However, placing an LLM on the critical path of every scheduling decision is impractical: for a 200-service fleet, per-decision inference would cost more than the cloud capacity it manages and add multi-second latency to a millisecond-scale control loop. HELIOS moves the LLM off the critical path. The LLM evolves executable orchestration policies, small Python programs over a fixed feature interface, against a trace-driven multi-cloud simulator. Only the champion program runs in production, wrapped in guardrails that enforce capacity feasibility, SLO-class placement rules, and churn budgets regardless of evolved code's proposal. On real workload traces (PlanetLab, Bitbrains, Azure) combined with 2026 price and interruption-frequency data from AWS, Azure, and GCP, the evolved policy reduces penalized operating cost by 45% versus a single-cloud best-fit baseline and by 9-19% versus calibrated multi-cloud heuristics. It matches an oracle-informed MILP planner's premium-SLO performance at 40% lower cost and outperforms a DQN meta-controller trained with 2.6x more environment interactions. Guardrails are essential: without them, the same policy family degrades to 97-98% premium-tier downtime under a 10x spot-hazard stress test, versus 0.17% when guarded. We release the simulator, policies, LLM prompt/response archive, and per-generation fitness logs for full reproducibility.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-10 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.09164 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T07:25:19.007Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).