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Modern Data Management Overview. Storage Resource Broker. Reagan W. Moore Topics. Data management evolution Shared collections Digital Libraries Persistent Archives Building shared collections Project level / National level / International

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modern data management overview

Modern Data Management Overview

Storage Resource Broker

Reagan W. Moore

  • Data management evolution
    • Shared collections
    • Digital Libraries
    • Persistent Archives
  • Building shared collections
    • Project level / National level / International
  • Demonstration of shared collections
    • Access to collections at SDSC
types of data management
Types of Data Management
  • File system (AFS)
    • Provides caching at remote sites, uses single authentication system
  • Backup system (Veritas)
    • Provides time-based copies of data, tools for re-loading backups
  • Database system (Oracle 10g IFS)
    • Can link metadata to files on an Internet File System
  • Archive system (HPSS)
    • Manages data stored on tape, supports parallel I/O streams
  • Persistent object environment (Avaki)
    • Provides vaults for storing objects
  • Globus toolkit
    • Provides differentiated services for building a data grid
data management environments
Data Management Environments
  • Data grids
    • Manage shared collections
  • Digital libraries
    • Provide discovery, browsing, presentation services on top of collections
  • Persistent archives
    • Manage technology evolution while the authenticity and integrity of the assembled collection is preserved
  • Real-time sensor networks
    • Manage access to real-time data streams from thousands of sensors
generic infrastructure
Generic Infrastructure
  • Can a single system provide all of the features needed to implement each type of data management system, while supporting access across administrative domains and managing data stored in multiple types of storage systems?
  • Answer is data grid technology
types of data management6
Types of Data Management
  • File system (AFS)
    • Data grid manages replication, parallel I/O, containers
  • Backup system (Veritas)
    • Data grid supports replicas, versions, and snapshots of files and containers
  • Database system (Oracle 10g IFS)
    • Data grid virtualizes catalogs - schema extension, bulk metadata load
  • Archive system (HPSS)
    • Data grid integrates access across archives and file systems
  • Persistent object environment (Avaki)
    • Data grid manages user-defined metadata and collection hierarchy
  • Globus toolkit - set of differentiated services
    • Data grid manages consistent state information
shared collections
Shared Collections
  • Purpose of SRB data grid is to enable the creation of a collection that is shared between academic institutions
    • Register digital entity into the shared collection
    • Assign owner, access controls
    • Assign descriptive, provenance metadata
    • Manage state information
      • Audit trails, versions, replicas, backups, locks
      • Size, checksum, validation date, synchronization date, …
    • Manage interactions with storage systems
      • Unix file systems, Windows file systems, tape archives, …
    • Manage interactions with preferred access mechanisms
      • Web browser, Java, WSDL, C library, …
federated server architecture
Federated Server Architecture

Peer-to-peer Brokering

Read Application

Parallel Data Access

Logical Name


Attribute Condition











SRB agent

SRB agent


Server(s) Spawning



1.Logical-to-Physical mapping

2.Identification of Replicas

3.Access & Audit Control


Data Access

generic infrastructure9
Generic Infrastructure
  • Digital libraries now build upon data grids to manage distributed collections
    • DSpace digital library - MIT and Hewlitt Packard
    • Fedora digitial library - Cornell University and University of Virginia
  • Persistent archives build upon data grids to manage technology evolution
    • NARA research prototype persistent archive
    • California Digital Library - Digital Preservation Repository
    • NSF National Science Digital Library persistent archive
southern california earthquake center
Southern California Earthquake Center

Select Receiver (Lat/Lon)


Time History


Select Scenario

Fault Model

Source Model

SCEC Community Library

  • Intuitive User Interface
    • Pull-Down Query Menus
    • Graphical Selection of Source Model
    • Clickable LA Basin Map (Olsen)
    • Seismogram/History extraction (Olsen)
  • Access SCEC Digital Library
    • Data stored in a data grid
    • Annotated by modelers
    • Standard naming convention
    • Automated extraction of selected data and metadata
    • Management of visualizations

SCEC Digital Library

terashake data handling
Terashake Data Handling
  • Simulate 7.7 magnitude earthquake on San Andreas fault
    • 50 Terabytes in a simulation
    • Move 10 Terabytes per day
  • Post-Processing of wave field
    • Movies of seismic wave propagation
    • Seismogram formatting for interactive on-line analysis
    • Velocity magnitude
    • Displacement vector field
    • Cumulative peak maps
    • Statistics used in visualizations
    • Register derived data products into SCEC digital library






Wind Speed





ROADNet Sensor Network Data Integration



Rain start

Fire start

Frank Vernon - UCSD/SIO

chile june 13 2005
Chile June 13, 2005

Mw 7.9

Frank Vernon - UCSD/SIO

national science digital library
National Science Digital Library
  • URLs for educational material for all grade levels registered into repository at Cornell
  • SDSC crawls the URLs, registers the web pages into a SRB data grid, builds a persistent archive
    • 750,000 URLs
    • 13 million web pages
    • About 3 TBs of data
worldwide university network data grid
Worldwide University Network Data Grid
  • SDSC
  • Manchester
  • Southampton
  • White Rose
  • NCSA
  • U. Bergen
  • A functioning, general purpose international Data Grid for academic collaborations

Manchester-SDSC mirror

kek data grid
KEK Data Grid
  • Japan
  • Taiwan
  • South Korea
  • Australia
  • Poland
  • US
  • A functioning, general purpose international Data Grid for high-energy physics

Manchester-SDSC mirror

babar high energy physics
BaBar High-energy Physics
  • Stanford Linear Accelerator
  • Lyon, France
  • Rome, Italy
  • San Diego
  • RAL, UK
  • A functioning international Data Grid for high-energy physics

Manchester-SDSC mirror

Moved over 100 TBs of data

astronomy data grid
Astronomy Data Grid
  • Chile
  • Tucson, Arizona
  • NCSA, Illinois
  • A functioning international Data Grid for Astronomy

Manchester-SDSC mirror

Moved over 400,000 images

storage resource broker 3 3 1
Storage Resource Broker 3.3.1






Databases -

DB2, Oracle,

Sybase, Postgres,

mySQL, Informix

File Systems

Unix, NT,


Archives - Tape,



UniTree, ADS













Linux I/O


NT Browser,

Kepler Actors

Federation Management

Consistency & Metadata Management / Authorization, Authentication, Audit





Logical Name




Database Abstraction

Storage Repository Abstraction

Databases -

DB2, Oracle, Sybase,

Postgres, mySQL,



srb objectives
SRB Objectives
  • Automate all aspects of data discovery, access, management, analysis, preservation
    • Security paramount
    • Distributed data
  • Provide distributed data support for
    • Data sharing - data grids
    • Data publication - digital libraries
    • Data preservation - persistent archives
    • Data collections - Real time sensor data
srb developers
SRB Developers

Reagan Moore - PI

Michael Wan - SRB Architect

Arcot Rajasekar - SRB Manager

Wayne Schroeder - SRB Productization

Charlie Cowart - inQ

Lucas Gilbert - Jargon

Bing Zhu - Perl, Python, Windows

Antoine de Torcy - mySRB web browser

Sheau-Yen Chen - SRB Administration

George Kremenek - SRB Collections

Arun Jagatheesan - Matrix workflow

Marcio Faerman - SCEC Application

Sifang Lu - ROADnet Application

Richard Marciano - SALT persistent archives

75 FTE-years of support

About 300,000 lines of C

  • 1995 - DARPA Massive Data Analysis Systems
  • 1997 - DARPA/USPTO Distributed Object Computation Testbed
  • 1998 - NSF National Partnership for Advanced Computational Infrastructure
  • 1998 - DOE Accelerated Strategic Computing Initiative data grid
  • 1999 - NARA persistent archive
  • 2000 - NASA Information Power Grid
  • 2001 - NLM Digital Embryo digital library
  • 2001 - DOE Particle Physics data grid
  • 2001 - NSF Grid Physics Network data grid
  • 2001 - NSF National Virtual Observatory data grid
  • 2002 - NSF National Science Digital Library persistent archive
  • 2003 - NSF Southern California Earthquake Center digital library
  • 2003 - NIH Biomedical Informatics Research Network data grid
  • 2003 - NSF Real-time Observatories, Applications, and Data management Network
  • 2004 - NSF ITR, Constraint based data systems
  • 2005 - LC Digital Preservation Lifecycle Management
  • 2005 - LC National Digital Information Infrastructure and Preservation program
  • SRB 1.1.8 - December 15, 2000
    • Basic distributed data management system
    • Metadata Catalog
  • SRB 2.0 - February 18, 2003
    • Parallel I/O support
    • Bulk operations
  • SRB 3.0 - August 30, 2003
    • Federation of data grids
  • SRB 3.3.1 - April 6, 2005
    • Feature requests (extensible schema)
srb latency management
SRB Latency Management

Remote Proxies,


Data Aggregation









Client-initiated I/O


Parallel I/O


Server-initiated I/O

latency management bulk operations
Latency Management -Bulk Operations
  • Bulk register
    • Create a logical name for a file
    • Load context (metadata)
  • Bulk load
    • Create a copy of the file on a data grid storage repository
  • Bulk unload
    • Provide containers to hold small files and pointers to each file location
  • Bulk delete
    • Trash can
  • Sticky bits for access control, …
logical name spaces
Logical Name Spaces

Data Access Methods (C library, Unix, Web Browser)

  • Storage Repository
  • Storage location
  • User name
  • File name
  • File context (creation date,…)
  • Access constraints

Data access directly between

application and storage

repository using names

required by the local


logical name spaces31
Logical Name Spaces

Data Access Methods (C library, Unix, Web Browser)

Data Collection

  • Storage Repository
  • Storage location
  • User name
  • File name
  • File context (creation date,…)
  • Access constraints
  • Data Grid
  • Logical resource name space
  • Logical user name space
  • Logical file name space
  • Logical context (metadata)
  • Control/consistency constraints

Data is organized as a shared collection

federation between data grids
Federation Between Data Grids

Data Access Methods (Web Browser, DSpace, OAI-PMH)

Data Collection A

Data Collection B

  • Data Grid
  • Logical resource name space
  • Logical user name space
  • Logical file name space
  • Logical context (metadata)
  • Control/consistency constraints
  • Data Grid
  • Logical resource name space
  • Logical user name space
  • Logical file name space
  • Logical context (metadata)
  • Control/consistency constraints

Access controls and consistency constraints on cross registration of digital entities

types of risk
Types of Risk
  • Media failure
    • Replicate data onto multiple media
  • Vendor specific systemic errors
    • Replicate data onto multiple vendor products
  • Operational error
    • Replicate data onto a second administrative domain
  • Natural disaster
    • Replicate data to a geographically remote site
  • Malicious user
    • Replicate data to a deep archive
how many replicas
How Many Replicas
  • Three sites minimize risk
    • Primary site
      • Supports interactive user access to data
    • Secondary site
      • Supports interactive user access when first site is down
      • Provides 2nd media copy, located at a remote site, uses different vendor product, independent administrative procedures
    • Deep archive
      • Provides 3rd media copy, staging environment for data ingestion, no user access
state of the art technology
State of the Art Technology
  • Grid - workflow virtualization
    • Support execution of jobs (processes) across multiple compute servers
  • Data grid - data virtualization
    • Manage a shared collection that is distributed across multiple storage servers
  • Semantic grid - information virtualization
    • Create a common understanding of information (metadata) across multiple collections.
for more information
For More Information

Reagan W. Moore

San Diego Supercomputer Center