Jinlei JIANG

Education background

Bachelor of Computer Science & Technology, Tsinghua University, Beijing, China, 1999;

Ph.D. in Computer Science & Technology, Tsinghua University, Beijing, China, 2004.

Social service

Editorial Board Member: KSII Transactions on Internet and Information Systems (2013-)

Editorial Board Member: International Journal on Advances in Intelligent Systems (2012-)

PC Vice Co-Chair: FutureTech 2012

PC Member:SERVICE COMPUTATION, INTENSIVE (2009-);

PC Member:ADVCOMP, HPC China (2013-);

PC Member:FCST 2014, DCPIS 2013,Grid 2012, CloudCom 2012, CSE 2011;

Member: IEEE(2005-);

Member: ACM (2009-);

Member: China Computer Federation (CCF) Technical Committee on Information Storage (2015-)

Areas of Research Interests/ Research Projects

Big Data;

Cloud Computing;

Software-Defined Networking;

Distributed Computing and System;

Computer-Supported Cooperative Work;

Research Projects

National Key Research & Development Program of China: Contents Development for High-Performance Computing Education Services (2016-2018);

National Natural Science Foundation of China General Project: Research on the Key Technologies of Big Data Transfer (2016-2019);

National Natural Science Foundation of China Key Project:High Efficient Storage and Management for Big Data (2015-2019);

National Natural Science Foundation of China General Project:Research on the Key Technologies of HPC-oriented Platform Virtualization (2012-2015);

National 863 High-TechProgram: Network Operating System R&D for Public and Business Services (2011-2013);

National 863 High-Tech Program: Design and Implementation of a Scientific Computing Community over CNGrid (2009-2010).

Research Status

My research has mainly focused on cloud computing, big data storage and processing in recent years, with the following results achieved:

1. For cloud computing, targeting issues withclustersuch as monotonous usage mode, limited application types support, and managementcomplexity, I put forward a new virtualization model for multiple computing resources, which can support customization and auto deployment at the levels of hardware, operating system and applications, and developed a virtual computing system accordingly. In addition, I proposed a deduplication-based virtual machine (VM) image storage and transfer method that can both reduce space consumption and boost image distribution, and presented an approachfor fast live cross-datacenter VM migration.

2. For big data storage, I devised a method to combine SSD(solid state drive) and HDD(hard disk drive) to meet the requirements of big data processing and showed how to use it to accelerate MapReduce execution. Also, I studied the issues of cloud-based data storage and sharing, and co-developed MeePo, ahigh available cloud storage system that had been applied to China Unicom Guangdong Branch, Huawei, ZTE, CNPC, Chinese Academy of Sciences, Peking University and so on, serving more than 1.5 million registered users and 6000 enrolled communities in total, and that won the State Technological Invention Award, 2nd class in 2015.

3. For big data processing, to deal with MapReduce performance degradation on heterogeneous clusters, I put forward ActCap, a method that reduces inter-node data transfer(key cause of performance degradation) by node-capability-aware data placement; to improve MapReduce with skewed input data, I devised Skew--, a coordinated systematic solution that adopts complexity-aware keys assignment,a post-Map keys allocation schemetakinginto account not only the number of keys, but also the Reduce task complexity and the key group size to balance the loads among Reducers, and other data locality and resource scheduling enhancedmechanisms to guarantee the benefit of post-Map keys allocation. ActCap can gain an average speedup of 49.8% over standard Hadoop YARN and 9.8% over Tarazu, whereas Skew-- can get an average speedup of 1.98x over standard Hadoop YARN, and 1.63x, 1.77x, and 1.41x (Reduce phase only) over SkewTune, Online Balancer, and TopCluster respectively.

Honors And Awards

State Technological Invention Award, 2nd Class: High Available Cloud Storage System Oriented to Community Data Sharing (2015);

Alexander von Humboldt Foundation: Research Fellowship (2006);

FirstChina Symposium on Web Semantics and Ontology: Best Paper Award (2006);

Tsinghua University: Outstanding Ph.D. Dissertation Award (2004);

Chinese Journal of Software: 100 Best Reviewers (2004).

Academic Achievement

[1] Bo Wang, Jinlei Jiang, Yongwei Wu, Guangwen Yang, Keqin Li. Accelerating MapReduce on Commodity Clusters: An SSD-Empowered Approach. Accepted by IEEE Transactions on Big Data, 2016, DOI 10.1109/TBDATA.2016. 2599933

[2] Zuo-Ning Chen, Kang Chen, Jin-Lei Jiang, Lu-Fei Zhang, Song Wu, Zheng-Wei Qi, Chun-Ming Hu, Yong-Wei Wu, Yu-Zhong Sun, Hong Tang, Ao-Bing Sun, Zi-Lu Kang. Evolution of Cloud Operating System: From Technology to Ecosystem. Journal of Computer Science and Technology, 2017, 32(2): 224-241.

[3] Bo Wang, Jinlei Jiang and Guangwen Yang. ActCap: Accelerating MapReduce on Heterogeneous Clusters with Capability-Aware Data Placement. IEEE INFOCOM 2015: 1328-1336.

[4] Xun ZHAO, Yang ZHANG, Yongwei WU, Kang CHEN, Jinlei JIANG, Keqin LI, Liquid: A Scalable Deduplication File System for Virtual Machine Images.IEEE Transactions on Parallel and Distributed Systems, 2014, 25(5): 1257-1266.

[5] Jinlei Jiang, Yongwei Wu, Xiaomeng Huang, Guangwen Yang, Weimin Zheng. Online Video Playing on Smartphones: A Context-Aware Approach Based on Cloud Computing. Journal of Internet Technology, 2010, 11(6): 821-827.

[6] Jinlei Jiang, Shaohua Zhang, Johann Schlichter, Guangwen Yang. Workflow Management in the Grid Era: A Goal-driven Approach Based on Process Patterns. Multiagent and Grid Systems - An International Journal, 2009, 5(3): 325-343.

[7] Yushun Li, Shengwen Yang, Jinlei Jiang and Meilin Shi. Build Grid-enabled Large-scale Collaboration Environment in e-Learning Grid. Expert Systems With Applications, 2006, 31(4): 742-754.

[8] Jinlei Jiang, Shaohua Zhang, Yushun Li, Meilin Shi. CoFrame: A Framework for CSCW Applications Based on Grid and Web Services. ICWS 2005: 570-577.