185 citations · 193 across the 12 of their papers we have counts for
12 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…
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…
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Changfu Xu, Jianxiong Guo, Yuzhu Liang +7
Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and tr…
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…
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…
Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
Changfu Xu, Jianxiong Guo, Wanyu Lin +5
Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a cen…
Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing
Mengjie Liu, Yihua Li, Fangyi Mou +4
Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security an…
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…