100 citations · 234 across the 21 of their papers we have counts for
21 papers
BeeFlow: Behavior Tree-based Serverless Workflow Modeling and Scheduling for Resource-Constrained Edge Clusters
Ke Luo, Tao Ouyang, Zhi Zhou +1
Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy sup…
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Liekang Zeng, Xu Chen, Peng Huang +3
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.…
Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Huirong Ma, Zhi Zhou, Xiaoxi Zhang +1
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among…
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…
iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud
Fei Xu, Jianian Xu, Jiabin Chen +4
GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs…
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Liekang Zeng, Chongyu Yang, Peng Huang +3
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has ext…
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
Peng Huang, Liekang Zeng, Xu Chen +3
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the communit…
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
Qiong Wu, Xu Chen, Zhi Zhou +2
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and cap…
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
Qiong Wu, Xu Chen, Zhi Zhou +1
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and rec…
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
Liekang Zeng, Xu Chen, Zhi Zhou +2
Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computa…
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Shuai Yu, Xu Chen, Zhi Zhou +2
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilizati…
Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing
Huirong Ma, Zhi Zhou, Xu Chen
Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the chall…
Age of Processing: Age-driven Status Sampling and Processing Offloading for Edge Computing-enabled Real-time IoT Applications
Rui Li, Qian Ma, Jie Gong +2
The freshness of status information is of great importance for time-critical Internet of Things (IoT) applications. A metric measuring status freshness is the age-of-information (A…
HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
Deyin Liu, Xu Chen, Zhi Zhou +1
Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI applications. Conventional approaches to training DNNs are generally implemented at central se…
DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network
Qiong Wu, Muhong Wu, Xu Chen +3
Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for u…
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
Siqi Luo, Xu Chen, Qiong Wu +2
Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with…
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
En Li, Liekang Zeng, Zhi Zhou +1
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challen…
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou, Xu Chen, En Li +3
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to re…
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing
Tao Ouyang, Zhi Zhou, Xu Chen
Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of compu…
Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing
Xu Chen, Wenzhong Li, Sanglu Lu +2
Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at p…
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
En Li, Zhi Zhou, Xu Chen
As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, how…