1 / 13

Team

Team. Ghaleb Abdulla (0.4) Tina Eliassi-Rad (0.4) Terence Critchlow (0.15). Task Objective. Identify data storage formats that minimize access times using historical access patterns to the same or similar data sets

yosefu
Download Presentation

Team

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Team • Ghaleb Abdulla (0.4) • Tina Eliassi-Rad (0.4) • Terence Critchlow (0.15)

  2. Task Objective • Identify data storage formats that minimize access times using historical access patterns to the same or similar data sets Use spatial and temporal locality that result from data accesses to format the data on the disk

  3. Challenges • Data can be accessed using different: • tools, • by different users.

  4. Enabling Access Pattern Discovery • Application area (astrophysics) • Visualization tool (VisIt) • Analyze history of access patterns on two levels: • System Level • Disk references • Network overhead • Memory usage • Application level • Higher level commands • User level info

  5. Enabling Access Pattern Discovery [Pattern, Hints] training data User 1 User n VisIt Supervised Learner (e.g., neural net, DT, etc) Patterns Hints Application Logging Log files Unsupervised Learner (e.g., k-NN, k-means, etc) Disk Logging Djehuty Astrophysics

  6. Log file collection • Collect logs at the application and disk level • Managing log collection process • Start and stop collection sensors or agents based on demand • Keep log data in one central place • Detect any failure in the monitoring agents and restart them • Preferably work in a distributed environment JAMM from LBL meets our requirements

  7. JAMM Architecture

  8. What to Collect • Application and user level: • Open • Zoom • Slice • etc. • System level • Network overhead • Disk block size • Buffer size • Disk location, etc. We need to add our own sensors to collect data

  9. Data format • The DTD for our XML files is as follows: <!ELEMENT logfile (application+)> <!ELEMENT application (user+)> <!ATTLIST application name ID #REQUIRED> <!ELEMENT user (dataset+)> <!ATTLIST user name ID #REQUIRED> <!ELEMENT dataset (session+)> <!ATTLIST dataset name ID #REQUIRED> <!ELEMENT session (metadata+)> <!ATTLIST session time NMTOKENS #REQUIRED> <!ELEMENT metadata (#PCDATA)> <!ATTLIST metadata name ID #REQUIRED time NMTOKENS #IMPLIED>

  10. Log File, Example <?xml version="1.0" ?> <!DOCTYPE logfile(View Source for full doctype...)> <logfile>< application name="SimTracker">< user name="Tina">< dataset name=“astro "> <session time="01/11/2002 13:45:00 PST"> <metadata name="access_speed">100K</metadata> <metadata name="storage_utilization">0</metadata> <metadata name="cohesion">0</metadata> <metadata name="fault_tolerance">1</metadata> <metadata name="num_disks_to_strip">20</metadata> <metadata name="start_io_device">16</metadata> <metadata name="stripping_factor"time="01/11/2002 13:55:00">200</metadata> <metadata name="stripping_unit“ time="01/11/2002 13:59:00">64K</metadata> <metadata name="file_permissions">write</metadata> <metadata name="access_patterns">random</metadata> <metadata name="file_size">210M</metadata> <metadata name="io_buffer_size">128K</metadata> </session> </dataset></user></application></logfile>

  11. Data Analysis • Researched publicly available clustering tools • Narrowed our choice to two • CLUTO (University of Minnesota) • R (GNU) • Testing data processing algorithms on randomly generated log files • Hoping to get real log files in the near future: • Logging applications • We are currently looking at the “Flash” Log files

  12. Questions

  13. UCRL-MI-xxxxxx This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.

More Related