4 citations · 13 across the 11 of their papers we have counts for
11 papers
Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation
Hao Cheng, Shufeng Kong, Yanchen Deng +4
Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-reve…
MarS-FL: Enabling Competitors to Collaborate in Federated Learning
Xiaohu Wu, Han Yu
Federated learning (FL) is rapidly gaining popularity and enables multiple data owners ({\em a.k.a.} FL participants) to collaboratively train machine learning models in a privacy-…
Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning
Xiaohu Wu, Han Yu, Giuliano Casale +1
Premier cloud service providers (CSPs) offer two types of purchase options, namely on-demand and spot instances, with time-varying features in availability and price. Users like st…
Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance
Xiaohu Wu, Francesco De Pellegrini, Giuliano Casale
Many cloud service providers (CSPs) provide on-demand service at a price with a small delay. We propose a QoS-differentiated model where multiple SLAs deliver both on-demand servic…
Distributed Energy Trading and Scheduling among Microgrids via Multiagent Reinforcement Learning
Guanyu Gao, Yonggang Wen, Xiaohu Wu +1
The development of renewable energy generation empowers microgrids to generate electricity to supply itself and to trade the surplus on energy markets. To minimize the overall cost…
A Framework for Allocating Server Time to Spot and On-demand Services in Cloud Computing
Xiaohu Wu, Francesco De Pellegrini, Guanyu Gao +1
Cloud computing delivers value to users by facilitating their access to computing capacity in periods when their need arises. An approach is to provide both on-demand and spot serv…
Performance Analysis of QoS-Differentiated Pricing in Cloud Computing: An Analytical Approach
Xiaohu Wu, Francesco De Pellegrini
A fundamental goal in the design of IaaS service is to enable both user-friendly and cost-effective service access, while attaining high resource efficiency for revenue maximizatio…
Efficient Algorithms for Scheduling Moldable Tasks
Xiaohu Wu, Patrick Loiseau
We study the problem of scheduling independent moldable tasks on processors that arises in large-scale parallel computations. When tasks are monotonic, the best known resul…
Towards Designing Cost-Optimal Policies to Utilize IaaS Clouds under Online Learning
Xiaohu Wu, Patrick Loiseau, Esa Hyytia
Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon E…
Improved Competitive Analysis of Online Scheduling Deadline-Sensitive Jobs
Patrick Loiseau, Xiaohu Wu
We consider the following scheduling problem. There is a single machine and the jobs will arrive for completion online. Each job j is preemptive and, upon its arrival, its other ch…
Algorithms for Scheduling Malleable Cloud Tasks
Xiaohu Wu, Patrick Loiseau
Due to the ubiquity of batch data processing in cloud computing, the related problem of scheduling malleable batch tasks and its extensions have received significant attention rece…