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Dayu Is Now a KubeEdge Case Study

谢磊
Dayu TSC 主席 · 南京大学
周文晖
Dayu TSC 副主席兼维护者 · 南京大学

We are pleased to share that Dayu has been published as an official case study on the KubeEdge website. This milestone highlights how Dayu uses KubeEdge to support cloud-edge collaborative stream analytics.

New Accepted Paper: Steady scheduling

曹书与
Dayu 贡献者 · 南京大学
谢磊
Dayu TSC 主席 · 南京大学

Our paper "Tackling the Runtime Context Fluctuation: Steady Scheduling in Data Stream Analytics for Industrial Internet of Things" is Accepted by IEEE ICDCS 2026!

This paper proposes a steady scheduling framework based on the concept of side-effect for IIoT data stream analytics, which updates only configurations with small side-effects to shrink the search space, thereby maintaining already-met QoS objectives while re-meeting violated ones under runtime context fluctuations.

New Accepted Paper: Hier-EI

周文晖
Dayu TSC 副主席兼维护者 · 南京大学
谢磊
Dayu TSC 主席 · 南京大学

Our paper "Tackling the Imbalance in Video Analytics Pipelines with Hierarchical Embodied Intelligence" is Accepted by IEEE INFOCOM 2026!

This paper proposes Hier-EI (Hierahical Embodied Intelligence), an efficient scheduling framework that integrates a two-phase hierarchical design with embodied intelligence, which simultaneously considers spatial and temporal imbalance and enhance long-term QoE performance in cloud-edge collaborative video analytics pipelines.

Refer to homepage: https://dayu-autostreamer.github.io/hier-ei/.

New Accepted Paper: CRAVE

杨冰云
Dayu 贡献者 · 南京大学
谢磊
Dayu TSC 主席 · 南京大学

Our paper "Adaptive Region-aware Video Encoding for Real-time Cloud-edge Collaborative Object Detection" is Accepted by IEEE ICDCSW 2025!

This paper proposes CRAVE (Collaborative Region-aware Adaptive Video Encoding), which achieves efficient candidate region extraction through lightweight foreground detection and historical inference result analysis.

Welcome to Dayu

谢磊
Dayu TSC 主席 · 南京大学
周文晖
Dayu TSC 副主席兼维护者 · 南京大学

Welcome to Dayu, an auto scheduling system for stream data processing on distributed cloud-edge platforms.

We provide infrastructure for cloud-edge collaborative stream data analytics and focus on scheduling tasks like configuration optimization, model evolution, task offloading, and so on.