1 / 10

A Comparison of File System Workloads

A Comparison of File System Workloads . D. Roselli J. Lorch T. Anderson University of California, Berkeley. Motivation. Wanted to find common file usage patterns Focused on how workload and file system parameters affect disk behavior Cache sizes Memory mapping Delayed write policy

cassandra
Download Presentation

A Comparison of File System Workloads

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. A Comparison ofFile System Workloads D. Roselli J. Lorch T. Anderson University of California, Berkeley

  2. Motivation • Wanted to find common file usage patterns • Focused on how workload and file system parameters affect disk behavior • Cache sizes • Memory mapping • Delayed write policy • File layout policies

  3. Trace collection • Three Unix environments: • 20 workstations used for undergraduate classes • 13 workstations usedby faculty, graduate students and staff working on research projects • One web server • One set of eight desktops running Windows NT

  4. Trace collection issues • Modified Unix kernel in order to record full pathnames of all files being accessed • Auditing subsystem of HP-UX only recorded file names as specified by the user • Often relative to current directory

  5. Estmating block lifetimes (I) • A previous study measured block lifetimes as delete time minus create time • This delete-based method • Did not take into account the lifetimes of blocks that were not deleted during measurement interval • Underestimated block lifetimes

  6. Estimating block lifetimes (II) • Create-based method partitions trace into two parts • First part is used to collect information on block creation times • Proceed as in delete-based method for blocks that were deleted during total observation period • Assume that other blocks have lived for at lest the duration of the second part of the trace

  7. Example D2 X Time Trace starts Create Block B First part of trace ends Trace ends If block B was never deleted while trace was collected, its lifetime must be greater than D2

  8. Conclusions (I) • Different systems show • Different I/O loads • Different block lifetimes and lifetime distributions

  9. Conclusions (II) • Block overwrites • Cause the most significant fraction of deleted blocks • Show substantial locality • A small write buffer is sufficient for nearly all workloads • Ideal write delay vary with workload • Standard 30 second delay • Slightly longer delays

  10. Conclusions (III) • Even small caches can sharply reduce disk read traffic • Very large caches will not cause disk traffic to be dominated by writes • A small number of memory mapped files are shared among many active processes • Should keep file in memory as long as it is memory-mapped by any process

More Related