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Author

Vincenzo Lomonaco

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Open access Sep 2026

On-board efficiency: comparing compressed deep learning and classical models for Earth observation

A unified experimental framework is introduced that jointly evaluates compressed deep learning models and CML ensembles on two representative EO benchmarks and provides empirical guidance for selecting model families and compression strategies when designing future on-board EO systems.

G. Di Palma, Alessio Pardini, Lan-Pei Li et al. · 0 citations
Preprint Sep 2026

ContinuumBench: Benchmarking Joint Autoscaling and Placement Across Evaluation Regimes in the Cloud-Edge Continuum

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.

Lan-Pei Li, Antonino Vaccarella, Vincenzo Lomonaco et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management

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.

Antonino Vaccarella, Lan-Pei Li, Vincenzo Lomonaco et al. · 0 citations

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