29 citations · 127 across the 19 of their papers we have counts for
20 papers
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs
Menglu Yu, Ye Tian, Bo Ji +3
Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL com…
Toward Efficient Online Scheduling for Distributed Machine Learning Systems
Menglu Yu, Jia Liu, Chuan Wu +2
Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. How…
A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling
Menglu Yu, Chuan Wu, Bo Ji +1
In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scal…
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan, Yi Rong, Chen Meng +10
It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…
Distributed Machine Learning through Heterogeneous Edge Systems
Hanpeng Hu, Dan Wang, Chuan Wu
Many emerging AI applications request distributed machine learning (ML) among edge systems (e.g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to…
Characterizing Deep Learning Training Workloads on Alibaba-PAI
Mengdi Wang, Chen Meng, Guoping Long +4
Modern deep learning models have been exploited in various domains, including computer vision (CV), natural language processing (NLP), search and recommendation. In practical AI cl…
DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters
Yanghua Peng, Yixin Bao, Yangrui Chen +3
More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services. Efficient resource sched…
NFVactor: A Resilient NFV System using the Distributed Actor Model
Jingpu Duan, Xiaodong Yi, Shixiong Zhao +3
Resilience functionality, including failure resilience and flow migration, is of pivotal importance in practical network function virtualization (NFV) systems. However, existing fa…
Online Job Scheduling in Distributed Machine Learning Clusters
Yixin Bao, Yanghua Peng, Chuan Wu +1
Nowadays large-scale distributed machine learning systems have been deployed to support various analytics and intelligence services in IT firms. To train a large dataset and derive…
Normalized Direction-preserving Adam
Zijun Zhang, Lin Ma, Zongpeng Li +1
Adaptive optimization algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in some scenarios. However, recent stu…
Optimal Posted Prices for Online Cloud Resource Allocation
Zijun Zhang, Zongpeng Li, Chuan Wu
We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may v…
Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS
Jingpu Duan, Chuan Wu, Franck Le +2
The emerging paradigm of network function virtualization advocates deploying virtualized network functions (VNF) on standard virtualization platforms for significant cost reduction…
An Auction Approach to Spectrum Management in HetNets
Linquan Zhang, Zongpeng Li, Chuan Wu
The growing demand in mobile Internet access calls for high capacity and energy efficient cellular access with better cell coverage. The in-band relaying solution, proposed in LTE-…
Online Scaling of NFV Service Chains across Geo-distributed Datacenters
Yongzheng Jia, Chuan Wu, Zongpeng Li +2
Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on…
A Truthful -Optimal Mechanism for On-demand Cloud Resource Provisioning
Xiaoxi Zhang, Chuan Wu, Zongpeng Li +1
On-demand resource provisioning in cloud computing provides tailor-made resource packages (typically in the form of VMs) to meet users' demands. Public clouds nowadays provide more…
Online Influence Maximization in Non-Stationary Social Networks
Yixin Bao, Xiaoke Wang, Zhi Wang +2
Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influenti…
Online VNF Scaling in Datacenters
Xiaoke Wang, Chuan Wu, Franck Le +3
Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network fun…
Cost Minimization in Multiple IaaS Clouds: A Double Auction Approach
Jian Zhao, Chuan Wu, Zongpeng Li
IaaS clouds invest substantial capital in operating their data centers. Reducing the cost of resource provisioning, is their forever pursuing goal. Computing resource trading among…
Virtual Machine Trading in a Federation of Clouds: Individual Profit and Social Welfare Maximization
Hongxing Li, Chuan Wu, Zongpeng Li +1
By sharing resources among different cloud providers, the paradigm of federated clouds exploits temporal availability of resources and geographical diversity of operational costs f…
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
Yu Wu, Chuan Wu, Bo Li +1
As an important application in the busy world today, mobile video conferencing facilitates virtual face-to-face communication with friends, families and colleagues, via their mobil…