103 citations · 187 across the 16 of their papers we have counts for
16 papers
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
Guangji Bai, Ziyang Yu, Zheng Chai +2
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computin…
FS: A Scalable and Elastic Distributed File System Metadata Service using Serverless Functions
Benjamin Carver, Runzhou Han, Jingyaun Zhang +2
The metadata service (MDS) sits on the critical path for distributed file system (DFS) operations, and therefore it is key to the overall performance of a large-scale DFS. Common "…
SFS: Smart OS Scheduling for Serverless Functions
Yuqi Fu, Li Liu, Haoliang Wang +2
Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained serverless or cloud functions. The execution duration…
InfiniStore: Elastic Serverless Cloud Storage
Jingyuan Zhang, Ao Wang, Xiaolong Ma +8
Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides…
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization
Zheng Chai, Guangji Bai, Liang Zhao +1
Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many…
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Ahmad Faraz Khan, Yuze Li, Xinran Wang +5
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT ap…
Community-based Layerwise Distributed Training of Graph Convolutional Networks
Hongyi Li, Junxiang Wang, Yongchao Wang +2
The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the no…
Asynchronous Federated Learning for Sensor Data with Concept Drift
Yujing Chen, Zheng Chai, Yue Cheng +1
Federated learning (FL) involves multiple distributed devices jointly training a shared model without any of the participants having to reveal their local data to a centralized ser…
FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute
Ao Wang, Shuai Chang, Huangshi Tian +5
Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and scaling applications by allowing users to deploy fine-grained functions while providing ful…
Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing
Benjamin Carver, Jingyuan Zhang, Ao Wang +3
Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications. Executing complex, burst-parallel, direc…
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai, Yujing Chen, Ali Anwar +3
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new trade…
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problem…
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache
Ao Wang, Jingyuan Zhang, Xiaolong Ma +6
Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerg…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…
In Search of a Fast and Efficient Serverless DAG Engine
Benjamin Carver, Jingyuan Zhang, Ao Wang +1
Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges a…
Characterizing Co-located Datacenter Workloads: An Alibaba Case Study
Yue Cheng, Zheng Chai, Ali Anwar
Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges…