On-board processing is emerging as a key enabler for Earth observation (EO) missions, reducing downlink requirements and supporting more autonomous, event-driven operations. Deep convolutional neural networks (CNNs) deliver state-of-the-art performance on many EO tasks, but their memory footprint and computational demands remain challenging for space-qualified hardware. Classical machine learning (CML) pipelines based on hand-crafted spectral and textural features offer a lighter alternative, yet it is unclear how they compare with modern compressed deep models under deployment-relevant efficiency metrics. This work introduces a unified experimental framework that jointly evaluates compressed deep learning (CDL) models and CML ensembles on two representative EO benchmarks: EuroSAT for land-cover classification and HYPERVIEW for hyperspectral soil-property regression. Starting from a common CNN baseline, we evaluate pruning and post-training quantization, and use the selected baseline as the teacher in family-conditioned KD experiments. We contrast the resulting models with optimized tree-based ensembles trained on engineered features. For the benchmark comparisons we measure predictive efficacy, inference time and serialized model size, enabling a systematic comparison of the trade-offs between accuracy, runtime and storage. In our experiments, quantization provides the largest observed storage reduction among the tested CDL variants, moderate pruning can preserve predictive performance more closely in some settings, and the effectiveness of knowledge distillation depends more strongly on the dataset, student design, and distillation setting. Classical ensembles remain attractive when low prediction-stage latency or small serialized models are required and a moderate loss in accuracy is acceptable. For raw-input deployment, their runtime benefit also depends on the cost and implementation of descriptor extraction. The proposed analysis 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.· Pattern Analysis and Applica...· 0 citations
Cloud-edge controllers coordinate service placement, replica scaling, and resource pre-warming to keep end-to-end latency within application deadlines. But evaluations often obscure the source of a reported gain: placement and scaling are studied separately; workload, connectivity, and calibration assumptions remain implicit; and metrics over completed tasks hide unfinished work. We present ContinuumBench, a benchmark that controls these factors. Its completion-aware accounting treats late, unfinished, and discarded tasks as deadline misses. A common protocol compares placement-only and scale-capable controllers under declared regimes and stressors. Built on the ECLYPSE simulator, ContinuumBench adds arrivals, worker elasticity, intermittent transport, buffering, and failures to close the control loop. We evaluate nine controllers across four scenarios and two regimes. The studied regimes are capacity-bound: elastic capacity, not placement sophistication, drives completion, and once capacity suffices, the choice of autoscaling policy decides how much of that work arrives on time. Placement re-planning has no measurable effect without relocation, while cost-free migration defines the observed exception. Consequently, scale-capable controllers approach an over-provisioned reference while placement-only controllers degrade with load; and placement quality separates controllers only once capacity is exhausted. Finally, the accounting choice itself changes the reported result: completion-only and completion-aware scoring can rank controllers differently.
Lan-Pei Li, Antonino Vaccarella, Vincenzo Lomonaco et al.· 0 citations
Managing resources across IoT, edge, and cloud layers calls for continuous, context-aware decisions under constraints that rarely stay fixed. Deep reinforcement learning (DRL) handles this class of problems well, and large language models (LLMs) are increasingly used to augment DRL pipelines, yet the architectural relationship between the two is seldom made explicit. We build on Wang et al.'s taxonomy of Continuum Orchestration Systems employing DRL techniques and extend it with two further dimensions. The AI Augmentation Paradigm measures how LLMs are exploited, while the Feedback channel captures whether and through which system path the execution feedback returns to the LLM in order to close the MAPE control loop at the LLM Orchestration layer. We apply this taxonomy to six recent system architectures and find a common gap, as none combines full LLM orchestration with full agent-layer feedback in a Cloud Continuum setting. We relate this gap to a missing cross-tier feedback abstraction, 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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