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My research interests include distributed computing, virtualization,
high-performance systems, autonomic computing, cyberinfrastructure, and operating systems. I am also interested in the interdisciplinary studies that bridge computer systems research with other domains such as computational sciences, medicine, and business.
Today's large distributed applications require tremendous amounts of data and resources, supported by computer systems with increasing scale, complexity, and dynamism. It is often difficult to make effective use of resources and execute applications with desired
quality-of-service in such environments. My goal is to explore novel data and resource management solutions, through innovating virtualization, grid, and autonomic techniques, to provide high-performance, robust, and secure computing.
Current Projects
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Scalable Parallel File System and Qos-driven Storage Management for High-end Computing Systems
Parallel storage systems are the backbone of high-end computing (HEC) systems. With the ever-increasing demand from HEC applications and the ever-increasing size of HEC systems, current parallel storage systems are becoming insufficient to support their Quality of Service (QoS) and scalability needs. This project studies novel parallel file system and storage management techniques to address these challenges.
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Autonomic Resource Management of Virtualized Computing Systems
On-demand computing with networked data-centers poses challenging problems of delivering expected QoS and doing so with acceptable management complexity. This project studies autonomic architectures and mechanisms for cooperative resource allocation and QoS delivery by virtualized data centers, in which application services are provided in virtual-machine based execution environments.
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Grid Virtual File System
GVFS is a virtualized distributed file system that provides high-performance and secure data access in
grid/wide-area environments, and allows for seamless integration with unmodified applications. GVFS helps
users access grid data in the same way as using traditional LAN file systems. It is also an integral part
of the In-VIGO grid virtualization middleware.
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Data Provisioning for Grid Computing on Virtualized End-Resources
Grid computing with virtual machines promises the capability of provisioning a secure and highly flexible
computing environment for users. This project develops middleware to provide efficient data management
service for grid VMs (for both VM states and user data). It can be applied to VMs of different kinds
(VMware, Xen, UML etc.).
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Past Projects
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Cyberinfrastructure for Dynamic Data-driven Brain-machine Interfaces (DDD-BMI)
The goal of DDD-BMI is to model brain from experiments with live subjects and to design brain-inspired assistive systems. This project develops a middleware-based cyberinfrastructure to support this time-critical and resource-demanding application. The resulting cyber-workstation enables BMI experiments to be conducted in a closed-loop manner, including in vivo brain signals acquisition, reliable network messging, parallel BMI computing, and real-time robot control.
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Fine-Grain Grid Data Management and Applications in LSS Medical Imaging
This project aims to improve the state-of-the-art in data management techniques for grids to
allow seamless and high-performance integration of data generated by medical devices with
distributed computers. It enables the deployments of network-computing based medical
applications (e.g. Light-Scattering Spectroscopy analysis) for early cancer detection.
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