We introduce Adaptive Score-based Routing Balancer (ASRB), a dynamic, score-based request routing mechanism for Kubernetes-based service deployments over the computing continuum. ASRB jointly considers infrastructure-level information, response time measurements, and application-level quality indicators, with a particu...
Ignjat Karanovic, P. Frangoudis, Ivan Čilić et al.· 0 citations
Test-Time Adaptation (TTA) aims to adapt pretrained models to unseen test data, which is crucial for resource-constrained edge devices that must handle distribution shifts on the fly without human supervision. However, conventional TTA methods often fail in realistic scenarios characterized by continuous shifts and sev...
Hao-Jie Bai, Aiguo Chen, Rui-Ting Dai et al.· Proceedings of the Thirty-Fi...· 0 citations
With the rapid proliferation of 5G and the Internet of Things, ensuring low latency in edge computing has become crucial for real‐time processing applications. However, existing task scheduling approaches often struggle to balance multiple optimization objectives effectively due to manual parameter tuning and slo...
Yu-Qi Zhao, Shen-Dong Gao, Ya-Tong Wang et al.· Software, Practice & Experie...· 0 citations
Federated Learning (FL) enables collaborative model training across distributed devices. A major concern in FL is how to operate it smoothly in resource-constrained environments, where this process must perform under strict operational constraints, such as cost or energy budgets. An often-overlooked aspect is the effec...
Anna Lackinger, P. Frangoudis, Andrea Morichetta et al.· 2026 International Conferenc...· 0 citations
This article categorizes existing UAV inspection architectures, identifies their key system challenges and architectural requirements, and experimentally assesses the feasibility of semantic edge intelligence on NVIDIA Jetson UAV-class hardware using the COCO-Bridge dataset.
Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited and uneven capacity. This balance becomes especially difficult when resource scaling reaches capacity limits, because changes in demand and cluster pressure must then be ab...
Javier Mateos-Bravo, S. Laso, Juan Luis Herrera et al.· 0 citations
This paper benchmarks the performance trade-offs among fully onboard, cloud-based, and split-computing architectures for lightweight VLMs using SmolVLM-256M as a representative lightweight VLM and shows that no deployment strategy is universally optimal.
Zoha Azimi, Reza Farahani, S. Dustdar et al.· 0 citations
Federated edge learning (FEEL) is a prospective paradigm enabling edge devices to collaboratively participate in machine learning model training, unlocking countless opportunities for edge intelligence. As an extension of FEEL, federated synergy learning (FSyL) alleviates the computation and communication burdens on re...
Shu-Cun Fu, Fang Dong, Xiao-Long Xu et al.· IEEE Transactions on Mobile...· 1 citation
This work trains a neural codec with a clear-probability-weighted reconstruction loss, reallocating coded bytes from clouds to clear ground without requiring or transmitting a cloud map onboard.
Alireza Furutanpey, Qi-Yang Zhang, Yujie Huang et al.· 0 citations
DRLM, a Deep Reinforcement Learning-based LLM query orchestration framework in edge environments shows robust and stable orchestration, and improves latency under increasing workloads up to 61.4%, demonstrating robust and stable orchestration.
Reza Farahani, Zoha Azimi Ourimi, Mario Colosi et al.· 0 citations
The Federated Parallel Scaling (FPS) algorithm is proposed, which jointly trains multiple sampled subnetworks in parallel with self-distillation so that larger sampled subnetworks can supervise smaller ones during local updates.
Jia-Xin Zhang, Xing-Wei Wang, Bo Yi et al.· 0 citations
Performance evaluation is essential for understanding, comparing, and improving computing systems, including Distributed Computing Continuum Systems (DCCS). In recent years, computational requirements have changed substantially with the growth of artificial intelligence and large-scale data-driven applications. These a...
Praveen Kumar Donta, Boris Sedlak, Alfreds Lapkovskis et al.· arXiv.org· 0 citations
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