Easing the Burdens of HPC File Management Stephanie Jones , - - PowerPoint PPT Presentation

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Easing the Burdens of HPC File Management Stephanie Jones , - - PowerPoint PPT Presentation

Mar 31, 2023 44 likes •309 views

Easing the Burdens of HPC File Management Stephanie Jones , Christina Strong, Aleatha Parker-Wood, Alexandra Holloway, Darrell D. E. Long UC Santa Cruz Introduction Scientists waste time on manual metadata management Manual

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Stephanie Jones, Christina Strong, Aleatha Parker-Wood, Alexandra Holloway, Darrell D. E. Long UC Santa Cruz

Easing the Burdens of HPC File Management

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Introduction

  • Scientists waste time on manual metadata

management

  • Manual correlation of related files and data
  • Remembering which storage system data is on
  • Lengthy file names with human error
  • Time spent searching for the right data
  • We propose a provenance enabled file system to

prevent this

  • Provenance integrated with traditional metadata
  • Unified search space over primary and archival storage
  • Generate descriptive names for files
  • Query results ranked by importance to the scientist

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Introduction

  • Interviewed scientists from LANL, PNNL and

NOTUR

  • File system use is not always “correct”
  • Some scientists store everything on a parallel file system
  • Others only keep static files on NFS
  • Results in
  • Degradation of file system performance
  • Managing loads not designed to handle
  • Scientists managing their own metadata information

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File Organization Methods

  • Physical copies
  • Handwritten metadata information in notebooks
  • Print outs of typed metadata information in binders
  • Electronic copies
  • PowerPoint presentations
  • Spreadsheets
  • Encode metadata into file or directory names
  • Text files
  • Scientists feel the file system is lacking
  • Not recording the information needed
  • Easier to manage own data than find it again

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An Ideal System

  • Scientists don’t want to worry about where their

data is stored

  • Care that it is stored, and that they can get it back
  • Query metadata specific to their experiments
  • Coordinates with a specific temperature
  • Create tags to classify and search their data
  • Tag all files used in an experiment
  • Relationships between files are created

automatically

  • Visualization file came from this output data

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Background

  • Data provenance
  • Content based
  • Information flow
  • Workflow provenance
  • Prospective
  • Retrospective
  • File system ranking
  • Use existing metadata
  • Create new metadata by inferring links between files
  • Manually specify relationships between files

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Provenance Enabled File System

  • Data provenance will track file relationships for

scientists

  • Provenance can be used to create a unified

search space over multiple storage systems

  • Provenance and traditional metadata can be used

to create descriptive names for files

  • Rank search results to more accurately return

useful files from queries

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Provenance in HPC

  • No known implementations of a provenance

enabled file system in HPC

  • However, provenance has a strong presence in

grid computing

  • Our work assumes provenance is collected and

available

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Storing Provenance

  • Common method for storing provenance is in a

database

  • MySQL
  • Berkeley DB
  • Traditional DBMS are often a poor solution for search

and indexing applications

  • Customized, application-specific performs better
  • Databases often optimized for read or write
  • We are exploring ways to add provenance to Ceph
  • Store provenance in the metadata server
  • Embed provenance data in files

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Unified Search Space

  • LANL has 3 different storage systems
  • PFS, NFS, HPSS
  • Querying each can be time consuming
  • Create a unified search space
  • A query must be able to access all types of metadata
  • A query must be able to span all types of storage
  • Use transient provenance
  • Creates a record of archived data on primary storage

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Transient Provenance

  • Extension of information flow provenance
  • Tracks flow of data off a provenance aware system
  • Designed to help identify potential data leaks
  • Keeps a metadata record of data that leaves
  • Archiving moves data off of primary storage
  • Query over primary storage will include the metadata

records of archived data

  • Currently covers NFS and HPSS

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File Naming

  • Scientists often encode experiment parameters in

file or directory names

  • libego_alpha1_175_old_formula_uct2_final
  • Can result in many different problems
  • What was alpha?
  • Did I do old_formula or formula_old?
  • I can use 1024 characters on this system, but only 256 on

that one

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Generating File Names

  • Current issue
  • Multiple files with the same name
  • Low entropy
  • Obvious Solution
  • Use unique identifier for file names (i-node number)
  • High entropy
  • Better solution
  • Determine attributes that are both unique and interesting

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Choosing Attributes

  • Currently experimenting with techniques for

creating meaningful file names

  • Techniques from
  • linguistics (Zipf’s law)
  • faceted search
  • Data created by human behavior often follows

Zipfian distributions

  • Data created by automatic processes is often

random or uniform

  • Distribution of data will aid in identifying

meaningful attributes for file naming

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Preliminary Results

  • Naming MP3s
  • Zipf’s Law, high entropy, random
  • top three attributes
  • Zipf’s Law
  • 'Robert Kraft-Swing Kids-2008-06-18T06:23:53Z'
  • Artist - Album - Date Added
  • High Entropy
  • 'AF080E109902BB43-6895-file://localhost/Users/aleatha/Music/iTunes/

iTunes%20Music/Robert%20Kraft/Swing%20Kids/01%20Sing,%20Sing, %20Sing%20(with%20a%20Swing).mp3'

  • Persistent ID - Track ID - Location
  • Random
  • '6895-Robert Kraft-MPEG audio file'
  • Track ID - Artist - File type

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Query Result Ranking

  • With the massive amounts of data being

generated, need a way to find important data quickly

  • Naming files with uniquely identifying and

interesting information is half

  • Presenting data that is most important (to the

scientist) first

  • Think about a search on Google

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Why can’t we just use Google?

  • Google relies on the innate structure of the web
  • Links between pages are implicit endorsements
  • Current file system structures:
  • Directories?
  • Hard/soft links?
  • File names?
  • Access times?
  • None of these are actually endorsements
  • Without this, Google and other modern search

engines are just another similarity search

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Provenance and Search

  • Provenance tracks data flow, file opens, file closes
  • In combination with other metadata, can tell us:
  • How many people used a file
  • How recently
  • How often
  • Provenance can be used as an endorsement
  • What files people use
  • How often they use those files

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Leveraging PageRank

  • By interpreting provenance as endorsement, we

can leverage existing ranking algorithms like PageRank, and create new ones

  • How do we use the provenance graph to

determine usefulness?

  • PageRank uses links to determine the probability
  • f spending time on a page
  • Can do the same, but with some modifications

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PageRank

  • Stationary distribution of the Markov chain

described by the transition matrix of the web graph

  • What is the probability of arriving at a given page?
  • An infinite number of web surfers randomly click

links for an infinite amount of time

  • Surfers also have the ability to “teleport”
  • They get bored
  • They reach a page with no links

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Provenance Based Ranking

  • An infinite number of scientists randomly follow

inheritance links or open random files for an infinite amount of time.

  • Provenance graph is a Directed Acyclic Graph,

unlike the web, so teleport is more important

  • To reduce focus on the oldest files, the random file
  • pen is weighted to favor newer files

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Experiment B Experiment A Future_work.ppt workshop.pdf AGU.ppt nsf.pdf Viz Y Viz X Viz W Ancestors Children Viz Z USGS_invited.pdf

Provenance Graph Example

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Experiment A 0.23 Experiment B 0.15 workshop.pdf 0.05 Viz X 0.09 nsf.pdf 0.05 Viz W 0.16 Future_Work.ppt 0.05 Viz Y 0.07 Viz Z 0.07 AGU.ppt 0.05 USGS_invited.pdf 0.05

PageRank Example

  • The web has no roots per se
  • Provenance does
  • PageRank focuses too much on them

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Experiment A 0.08 Experiment B 0.05 workshop.pdf 0.10 Viz X 0.08 nsf.pdf 0.10 Viz W 0.15 Future_Work.ppt 0.10 Viz Y 0.07 Viz Z 0.07 AGU.ppt 0.10 USGS_invited.pdf 0.10

Provenance Rank example

  • Use teleport function weighted by the height of the

node

  • Can focus on more intermediate files

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Conclusions

  • Scientists need better tools to manage metadata

and search their data

  • Our system directly addresses the issues identified

by the scientists we talked with

  • Provenance information creates correlations among files
  • Automatically relates similar files
  • Unified search space allows scientists to find files
  • Regardless of which storage system they are stored on
  • Ranking query results increases search speed
  • Identifies files important to the scientist

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Acknowledgements

  • Scientists of LANL, PNNL and NOTUR for their time
  • Meghan Wingate McClelland for organizing the LANL

interviews

  • John Johnson for organizing the PNNL interviews
  • Department of Energy Office of Science
  • Department of Energy’s Petascale Data Storage Institute

(PDSI)

  • Center for Information Technology in the Interest of

Society (CITRIS)

  • NSF Center for Research in Intelligent Storage (CRIS)

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