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This work builds on Wang et al.'s taxonomy of Continuum Orchestration Systems employing DRL techniques and extends it with two further dimensions, measuring how LLMs are exploited and bridging the incommensurable per-tier signals and the LLM Orchestrator.
ContinuumBench is presented, a benchmark that controls workload, connectivity, and calibration assumptions and metrics over completed tasks hide unfinished work in cloud-edge controllers and compares placement-only and scale-capable controllers under declared regimes and stressors.
CLOUDADV slashes cloud costs by over 50% using zero-shot forecasting, proving that less can be more in cloud instance sizing.
Training a single routing model can now effectively navigate thousands of evolving AI models, outperforming fine-tuning and adapter-based approaches.
