185 citations · 193 across the 10 of their papers we have counts for
13 papers
LASER: Load-Aware Serving with Early-Exit for Reasoning LLMs at the Edge
Zhiqing Tang, Size Li, Hanshuai Cui +5
Large reasoning models (LRMs) such as DeepSeek-R1 have achieved strong performance through extended chain-of-thought (CoT) generation. However, deploying them on edge devices raise…
RISE: Relay Inference and Online Scheduling for Efficient Edge-Device Collaborative Diffusion Model Services
Zilan Huang, Zhiqing Tang, Hanshuai Cui +4
Text-to-image diffusion models are increasingly deployed at the network edge to serve heterogeneous workloads with diverse quality and latency requirements. However, existing deplo…
ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services
Kuan Yan, Zhiqing Tang, Tian Wang +1
Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deli…
CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG under Document Isolation
Xuedong Hu, Zhiqing Tang, Zhi Yao +2
Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time. As device-cloud collabo…
HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization
Size Li, Zhiqing Tang, Hongrui Liang +4
The proliferation of Large Language Models (LLMs) has shifted serving systems from processing isolated requests to orchestrating high-concurrency, multi-tenant agentic workflows. H…
Adaptive AI Agent Placement and Migration in Edge Intelligence Systems
Xingdan Wang, Jiayi He, Zhiqing Tang +5
The rise of LLMs such as ChatGPT and Claude fuels the need for AI agents capable of real-time task handling. However, migrating data-intensive, multi-modal edge workloads to cloud…
EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning
Zhifei Xu, Zhiqing Tang, Jiong Lou +5
The growth of Artificial Intelligence (AI) and large language models has enabled the use of Generative AI (GenAI) in cloud data centers for diverse AI-Generated Content (AIGC) task…
LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Zhiqing Tang, Wentao Peng, Jianxiong Guo +5
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further…
Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
Jinhao Sheng, Zhiqing Tang, Jianxiong Guo +1
The growing demand for real-time processing tasks is driving the need for multi-model inference pipelines on edge devices. However, cost-effectively deploying these pipelines while…
Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
Jingxi Lu, Wenhao Li, Jianxiong Guo +4
With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have become a lightweight and scalable solution. However, exi…
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
Xubin Wang, Zhiqing Tang, Jianxiong Guo +4
The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the…
Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application
Yandi Li, Jianxiong Guo, Yupeng Li +2
The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allo…
Joint Task Scheduling and Container Image Caching in Edge Computing
Fangyi Mou, Zhiqing Tang, Jiong Lou +3
In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that…