Distributed Systems Research


Cloud Computing Research

  • Predictive data grouping algorithms for optimizing content locality and server load balance.
  • Elastic data placement algorithms for optimizing server utilization.
  • Multi-model algorithms for on-line workload scalability.
    Publications

Data Mining Research

  • Leverage data mining algorithms in the design of large-scale distributed data management.
  • Time-series analysis: apply autoregressive and exponentially smoothing models for on-line history-based workload prediction.
  • Social network analysis: employ structural and dynamic network analysis for understanding data locality, data sharing and data popularity patterns.
  • Dynamic clustering algorithms for improving content locality.
    Publications

Peer-to-Peer Systems Research

  • Leverage collaborative classifications and multiple clustering for improving content locality.
  • Node-level and cluster-level self-organization for adapting to locality dynamics.
  • Efficient parallel lookup algorithm for high data recall, high tolerance to node failure, and avoidance of redundant communication.
    Publications

High Performance Computing Research


Large-scale I/O Research

  • Design, implement and evaluate a decentralized file cache hierarchy for PetaFLOP architectures.
  • Multiple-level data staging for PetaFLOP architectures: write-back and prefetching.
  • Study decentralized I/O coordination strategies for improving resource utilization in PetaFLOP architectures.
  • Insights for future Exascale architectures.
    Publications

Parallel I/O Optimizations Research

  • Design, implement and evaluate generic multiple-level parallel I/O staging algorithms.
  • Leverage data locality for optimal communication in collective I/O.
  • View-based non-contiguous I/O optimizations.
  • Inspector-executor collective I/O.
    Publications

Parallel File Systems Research

  • Generic data layouts for parallel files based on multi-dimensional data distribution algorithms from parallelizing compilers.
  • File system-level generalized file views.
  • File-system-level collective I/O (two-phase I/O and server-directed I/O).
  • File-system-level integration and study of cooperative caching and collective I/O.
  • Elastic distributed file system partitions.
  • Fault-tolerant models for parallel file systems.
  • Study decentralized parallel I/O scheduling in parallel file systems.
  • Novel MPI-IO implementation for Clusterfile and GPFS.
    Publications