arXiv AI By Qingzhong Li, Hui Ma, Yajun Zhang, Qingchang Ma, Zhou Long

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

Read the original on arXiv AI →

arXiv:2607. 22565v1 Announce Type: new Abstract: With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and reliability-critical applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 15

PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum

arXiv:2606. 14356v1 Announce Type: cross Abstract: Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural language processing into Compound AI systems.

By Milos Gravara, Cynthia Marcelino, Andrija Stanisic, Stefan Nastic
arXiv AI
Aug 19

Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum

The paper introduces a dynamic framework for partitioning neural network layers across a heterogeneous edge‑cloud continuum, adapting to runtime changes in network conditions and device capabilities. It profiles models at startup, measures link quality, and periodically re‑evaluates the partitioning to optimize performance. Experiments on a Raspberry Pi, laptop, and desktop using VGG16, AlexNet, and MobileNetV2 demonstrate energy savings of 27.09–35.82% and latency reductions of 6.34–22.92% over static partitioning.

By Akuen Akoi Deng, Eimantas Butkus, Alfreds Lapkovskis, Praveen Kumar Donta