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Contacts


Affiliated Projects

    Distributed Performance Management
    Features and Optimizations for LANL's PLFS
    Measuring Wikipedia
    Multiprocessor Real-Time Scheduling
    Parallel I/O interfaces for OpenMP
    Performance Mgmt of Data-intensive Computing
    PLFS (Parallel Log-structured File System)
    Real-time Memory Management
    Research on Storage QoS
    Scalable Data Management
    Searching Petascale File Systems
    Virtualizing Real-time Processing

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    Distributed Performance Management


    Performance monitoring and management of distributed systems.



    Kleoni Ioannidou
    UCSC Student

    Brandt

    Scott Brandt
    UCSC Instructor

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    Features and Optimizations for LANL's PLFS




    David Bonnie
    Clemson Student

    Grider

    Gary Grider
    Mentor

    Bent

    John Bent
    Mentor


    Aaron Torres
    Mentor


    Brett Kettering
    Mentor

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      Measuring Wikipedia


      Google funded project on information trust


      Pye

      Ian Pye
      UCSC Student

      Adler

      Bo Adler
      UCSC Student

      de

      Luca de Alfaro
      UCSC Instructor

      Spearing

      Shelly Spearing
      Mentor


      Jorge Roman
      Mentor

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      Multiprocessor Real-Time Scheduling




      Greg Levin
      UCSC Student

      Brandt

      Scott Brandt
      UCSC Instructor

      Top of page


      Parallel I/O interfaces for OpenMP




      Kshitij Mehta
      University of Houston Student


      Edgar Gabriel
      University of Houston Instructor

      Grider

      Gary Grider
      Mentor

      Bent

      John Bent
      Mentor


      Aaron Torres
      Mentor

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      Performance Mgmt of Data-intensive Computing


      "Much of today's IT infrastructure, including high‐performance systems, suffers
      from poorer and less predictable performance than necessary due to ineffective
      resource management. While processor performance is increasing at a rapid rate,
      increases in storage and memory performance are rather marginal, turning them
      into serious bottlenecks, particularly for data‐intensive applications. At the same
      time, memory and most storage subsystems operate in best‐effort mode without
      even minimal performance guarantees.

      We show that better and more predictable performance can be achieved by
      considering system resource characteristics. Our work on disk scheduling shows
      how this unpredictable resource can be effectively managed, and guaranteed, by
      changing the metric by which it is managed. An analysis of memory performance
      reveals a somewhat similar behavior to disk I/O with orders of magnitude
      performance difference between best and worst case. Thus, we propose to manage
      memory performance the same way, scheduling data‐intensive applications based
      on their memory access pattern.

      While analyzing system resource characteristics, we found that memory
      performance scales with parallel accesses. This can be exploited to predictably
      increase memory performance. We chose graphics processors with hundreds of
      cores as an example of massively‐parallel architectures, and databases as an
      example of data‐intensive applications to put the concept to the test. Our p‐ary
      search algorithm successfully demonstrates how using parallel memory accesses
      can yield predictable performance increases of up to 200%.


      Kaldewey

      Tim Kaldewey
      UCSC Student

      Brandt

      Scott Brandt
      UCSC Instructor

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      PLFS (Parallel Log-structured File System)


      He is working on developing and evaluating algorithms by which patterns in the PLFS (Parallel Log-structured File System) metadata can be discovered and then used to replace the current metadata, in order to reduce metadata size.



      Jun He
      IIT Student

      Grider

      Gary Grider
      Mentor

      Bent

      John Bent
      Mentor


      Aaron Torres
      Mentor


      Carlos Maltzahn
      Mentor

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      Real-time Memory Management


      My research focuses on buffer‐‐cache management for predictable I/O performance
      in local and distributed storage systems. The objective is to enable applications with
      predictable performance requirements (such as hard, firm, and soft real‐time) to
      have direct access to a storage system, and/or share it with applications with other
      performance requirements. Our approach consists on virtualizing the performance
      buffer‐cache, providing the illusion of a dedicated buffer‐cache to each application.


      Pineiro

      Roberto Pineiro
      UCSC Student

      Brandt

      Scott Brandt
      UCSC Instructor

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        Research on Storage QoS


        I am working on resource management algorithms for providing guaranteed
        performance to mixed workloads executing concurrently in a shared storage
        system.

        Guaranteed I/O performance is needed for a variety of applications ranging from
        real‐time data collection to desktop multimedia to large‐scale scientific simulations.
        Reservations on throughput, the standard measure of disk performance, fail to
        effectively manage disk performance due to the orders of magnitude difference
        between best‐, average‐, and worst‐case response times, allowing reservation of less
        than 0.01% of the achievable bandwidth. Moreover, hard I/O performance
        guarantees for a mix of workloads are generally considered impractical due to the
        stateful nature of disk I/O and the interference between workloads.

        Our research provides I/O performance guarantees for a mix of workloads with
        different performance and timeliness requirements without sacrificing
        performance. This is achieved via a novel disk I/O scheduler, Fahrrad, that makes
        hard performance guarantees in terms of disk time utilization. Building upon this
        base scheduler, our work addresses several key questions in real‐time disk I/O: how
        to provide isolation between concurrently executing request streams, the tradeoff
        between the tightness of the guarantees and the overall performance of the system,
        and how to use Fahrrad to make throughput and timeliness guarantees.


        Povzner

        Anna Povzner
        UCSC Student

        Brandt

        Scott Brandt
        UCSC Instructor

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          Scalable Data Management



          Buck

          Joe Buck
          UCSC Student

          Watkins

          Noah Watkins
          UCSC Student

          Brandt

          Scott Brandt
          UCSC Instructor

          Maltzahn

          Carlos Maltzahn
          UCSC Instructor

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          Searching Petascale File Systems



          This project is an LLNL/UCSC collaboration: the goal is to design a scalable
          metadata‐rich file system with database‐like data management services. With such a
          file system scientist will be able to perform time‐critical analysis over continually
          evolving, very large data sets.

          In the first phase we designed and implemented QUASAR, a path‐based query
          language using the POSIX IO data model extended by relational links. We conducted
          a couple of data mining case studies where we compared the baseline architecture
          consisting of a database and a file system with our MRFS prototype. The QUASAR
          interface via its query language provides much easier access to large data sets than
          POSIX IO. MRFS' querying performance is significantly better than the baseline
          system due to QUASAR's hierarchical scoping.

          Challenges remain and we are in the process of addressing them: we are working on
          a scalable physical data model of QUASAR's logical data model, and we are designing
          a rich‐metadata client cache to address small update overheads and metadata
          coherence.

          The work so far was presented at SOSP'07 (demo & poster), PDSW'07 (poster),
          PDSW'08 (poster), and reported in two 2008 tech reports available at the
          UCSC/PDSI site.


          Ames

          Sasha Ames
          UCSC Student

          Maltzahn

          Carlos Maltzahn
          UCSC Instructor

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          Virtualizing Real-time Processing


          I am interested in CPU virtualization. I am working on enabling more flexibility and
          control of CPU scheduling by separating user space and kernel space scheduling.


          Brandt

          Scott Brandt
          UCSC Instructor

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