Profiling Neural Network Partitioning Strategies for Inference across the Computing Continuum

Scalable AI Inference Pipelines Across Edge and Cloud Computing Environments

A scalable edge-to-cloud AI inference pipeline in which inference tasks are dynamically distributed across heterogeneous edge and cloud resources is examined, providing a basis for resilient real-time AI systems while highlighting unresolved challenges involving heterogeneous hardware, dynamic workloads, privacy-utility trade-offs, and cross-layer optimization.

GreenPipe: Power Modeling for Containerized DNN Inference on Kubernetes Edge Nodes

Distributed DNN inference is increasingly deployed in containerized edge-cloud environments, where workloads run on-device or are exposed to remote clients over the network. Accurate online power estimation on resource-constrained ARM nodes without hardware power counters such as RAPL remains a challenge, and CPU-only models fail to capture multi-resource behavior. We present GreenPipe, an automated profiling-training-validation pipeline that builds multi-resource regression models from external power meter measurements and attributes power to containers proportionally. GreenPipe is evaluated on a Raspberry Pi 4 edge node in a K3s edge-cloud testbed, covering DNN inference with three vision models, multiple precisions, thread counts, and both local and serving scenarios. System-level MAPE is 6.3-9.4%, improving over CPU-stress and utilization-only baselines by 26.9% MAPE on average. We jointly report inference latency and energy per inference, exposing performance-energy trade-offs across workload configurations.

Synchronous Distributed Training With Runtime-Adaptive Mechanisms in Hybrid Cloud Environments

The rapid growth of large datasets and AI models has necessitated scalable and powerful computing resources, driving the extension of training workloads to the cloud to balance cost and performance. Among distributed learning strategies, synchronous data-parallel training remains the most widely adopted approach due to its simplicity and stable convergence. However, heterogeneity between worker nodes and unreliability of network bandwidth pose substantial challenges. Since workers must synchronize gradients at every update step, even a single straggler can significantly degrade overall performance. In this paper, we present ASTRA, an auto-adaptive synchronous training framework tailored for hybrid cloud environments. ASTRA addresses these issues with three coordinated mechanisms: i) a network-topology detection module that clusters workers into groups by pairwise connection cost to enable hierarchical aggregation, where gradients are first aggregated within each group and then across groups, mitigating slow-link bottlenecks; ii) a load balancing module to efficiently distribute workloads among heterogeneous GPUs over time, reducing resource imbalance and straggler effects; and iii) an adaptive gradient compression module that dynamically adjusts compression levels based on network conditions to minimize inter-group communication overhead. Through automated adaptation to diverse system environments, our unified approach alleviates the burden of manual configuration for AI practitioners. Experimental evaluations demonstrate the effectiveness of the framework ASTRA, which achieves lower time-to-accuracy than a resource-heterogeneity-aware baseline and several compression-based frameworks, while preserving convergence quality and robustness across heterogeneous hybrid cloud environments.

Predicting CNN training latency on cloud GPUs without disclosing model architecture

DRLM: Deep Reinforcement Learning-Based LLM Query Orchestration in Edge Environments

Large language model (LLM) services increasingly process heterogeneous queries with diverse latency, accuracy, and resource requirements. While edge deployment reduces response time, the heterogeneity of devices and the diversity of model families, parameter scales, and quantization levels make efficient LLM query orchestration challenging. This paper introduces DRLM, a Deep Reinforcement Learning-based LLM query orchestration framework in edge environments. DRLM integrates two lightweight predictors: (i) a class-conditioned quality estimator that maps queries to semantic categories and infers model performance, and (ii) a feature-driven latency predictor that estimates inference time across model-device configurations. These predictions, combined with system state, feed a factorized Proximal Policy Optimization (PPO) agent that performs state-aware orchestration decisions. To enable data-driven orchestration, we construct a large-scale benchmarking dataset with 223 835 measurements spanning 1258 queries, 6 query classes, 8 model families (32 deployed instances), 5 quantization levels, and heterogeneous edge devices. Evaluation on a 64-node edge cluster and comparison with three baselines and two state-of-the-art methods show that DRLM reduces inference latency by up to 51% and queuing delay by up to 67 %, while incurring at most 8% accuracy loss. It improves latency under increasing workloads up to 61.4%, demonstrating robust and stable orchestration.

FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices

Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL methods typically bind training to a small predefined menu of model configurations, which limits architectural coverage. To address this bottleneck, we introduce Federated Adaptive Network Search (FANS), a hypernetwork-based framework that learns a shared architecture space rather than a fixed set of client models. To optimize this shared space efficiently, we propose the Federated Parallel Scaling (FPS) algorithm, which jointly trains multiple sampled subnetworks in parallel with self-distillation so that larger sampled subnetworks can supervise smaller ones during local updates. We evaluate FANS on CIFAR-10, CIFAR-100, and MNLI using ResNet-18, DenseNet-121, and BERT-base, respectively. Across all benchmarks, FANS expands the feasible subnetwork pool by orders of magnitude (e.g., 4,680 candidates for ResNet-18 vs. 4 in existing methods) and improves the average accuracy-efficiency trade-off relative to representative HFL baselines. Device heterogeneity is emulated through resource tiers, and evaluation covers accuracy, parameter count, and MACs.