Q
Qi Chen
Researcher at Peking University
Publications - 9
Citations - 1476
Qi Chen is an academic researcher from Peking University. The author has contributed to research in topics: Scalability & Virtualization. The author has an hindex of 7, co-authored 9 publications receiving 1324 citations.
Papers
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Journal ArticleDOI
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment
Zhen Xiao,Weijia Song,Qi Chen +2 more
TL;DR: This paper presents a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use and develops a set of heuristics that prevent overload in the system effectively while saving energy used.
Journal ArticleDOI
Adaptive Resource Provisioning for the Cloud Using Online Bin Packing
TL;DR: This paper presents an approach that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers actively used.
Journal ArticleDOI
Improving MapReduce Performance Using Smart Speculative Execution Strategy
Qi Chen,Cheng Liu,Zhen Xiao +2 more
TL;DR: In this article, a new speculative execution strategy, Maximum Cost Performance (MCP), is proposed to solve the straggler problem in MapReduce by simply back up those slow running tasks on alternative machines.
Journal ArticleDOI
LIBRA: Lightweight Data Skew Mitigation in MapReduce
Qi Chen,Jinyu Yao,Zhen Xiao +2 more
TL;DR: LIBRA as discussed by the authors is a lightweight strategy to address the data skew problem among the reducers of MapReduce applications, which does not require any pre-run sampling of the input data or prevent the overlap between the map and the reduce stages.
Journal ArticleDOI
Automatic Scaling of Internet Applications for Cloud Computing Services
Zhen Xiao,Qi Chen,Haipeng Luo +2 more
TL;DR: An efficient semi-online color set algorithm is developed that achieves good demand satisfaction ratio and saves energy by reducing the number of servers used when the load is low and is extremely scalable.