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Storage Resource Broker

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Storage Resource Broker Persistent Management of Distributed Data Reagan W. Moore General Atomics, Inc. San Diego Supercomputer Center [email protected] http://www.nirvanastorage.com
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Page 1: Storage Resource Broker

Storage Resource Broker Persistent Management of Distributed Data

Reagan W. MooreGeneral Atomics, Inc.

San Diego Supercomputer [email protected]

http://www.nirvanastorage.com

Page 2: Storage Resource Broker

Topics

• Data management systems– Data collections, digital libraries

• Distributed data management– Data grids

• Persistent data management– Persistent archives

• Common infrastructure for data management

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Data Collections• Astronomy

– CACR Computing Resource (NPACI)– National Virtual Observatory (NSF)– 2 Micron All Sky Survey (NPACI)– DPOSS Collection (NSF-NVO)– Hayden Planetarium

• Ecology and Environmental Sciences– CEED (NPACI)– Bionome– HyperLTER (NPACI)– Land Data Assimilation System– Knowledge Networks for BioComplexity (NSF)

• Medical Sciences– Digital Embryo (NLM)

• Molecular Sciences– JCSG, Synchrotron Data Repository (NSF)– AFCS, Alliance for Cell Signaling (NIH)

• NeuroSciences– Biomedical Information Research Network (NIH)– TeleScience Portal (NPACI)– Brain Databases (NPACI)– Brain Data Archiving (NPACI)

• Computer Science– DataStreaming (NPACI)– AppLeS, DataCutter (NPACI)

Page 4: Storage Resource Broker

•Physics and Chemistry–PPDG, Particle Physics Data Grid (DOE)–GriPhyN (NSF)–BaBar (DOE)–GAMESS (NPACI)

•Digital Libraries and Archives–SIO Digital Libraries (NSF)–California Digital Library–ADEPT (NSF)–Stanford Digital Library Project (NSF)–National Archives and Records Administration (NARA)

•Data Grids–ROADNet, Real-time Observatories App.and Data management–E-Science at CLRC, UK Grid Starter Kit (UK)–Library of Congress data grid–DOE ASCI Data Visualization Corridor–NASA Information Power Grid–DOE SciDAC - Portal Web Services–NPACI Portal Projects

•Education–Transana (NPACI)–Digital Insight (NPACI)–NSDL National STEM Education Digital Library (NSF)

Data Collections

Page 5: Storage Resource Broker

Data Collections

• Define the context for describing a collection of digital entities– Context specified by metadata attributes– Provenance, origin of the digital entities– Administrative, location of the digital entities– Technical, purpose of the digital entities

• Support organization of attributes as hierarchy of sub-collections

Page 6: Storage Resource Broker

Digital Libraries

• Provide services on the data collection– Ingestion, loading of attribute values– Extensibility, definition of new attributes– Discovery, queries on attributes– Browsing, hierarchical listing– Presentation, formatting specified data models

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Data Grids

• Manage data in a distributed environment– Logical name space, provide global identifier– Data access, storage system abstraction– Replication, disaster back up– Uniform access, common API across file

systems, archives, and databases– Single sign-on, authenticate across

administration domains

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Persistent Archives

• Manage technology evolution– Storage system abstraction, support data

migration across storage systems– Information repository abstraction, support

catalog migration to new databases– Logical name space, support global persistent

identifier

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SRB

• Integration of collection-based management of digital entities, with– Remote data access through storage system

abstraction– Catalog access through information repository

abstraction– Automation through collection-owned data

Storage Resource Broker

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Capabilities

• Support legacy systems• Integrate archives with file systems• Share distributed data• Maintain persistent collection• Control data access

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Digital Entities

• Digital entities are “images of reality” made of– Data, the bits (zeros and ones) put on a

storage system– Information, the attributes used to assign

semantic meaning to the data– Knowledge, the structural relationships

described by a data model• Every digital entity requires information

and knowledge to correctly interpret and display

Page 12: Storage Resource Broker

Digital Entities

• Files– Text documents, images, spread sheets, binary files

• URLs• Database query commands• Databases• Directories

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Digital Entities

• Register digital entities into a catalog• Assign metadata to describe each digital

entity• Separate management of the associated

data bits from management of the metadata

• Support manipulation of each digital entity data type

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Old Storage System

New Operating System

New Application

Digital Object

Old Display SystemWrap Storage System Wrap Display System

Migrate Encoding Format

Technology Management

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Preservation of Data

• Migration – Preserve the data bits– Preserve the digital entity name– Preserve the information and knowledge

content for presentation by new applications

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Migration Advantages

• By migrating the digital entity encoding format to new standards, more sophisticated technologies can be applied to express the information and knowledge content inherent in collections of digital entities.

• Requires the ability to associate data model with digital entity

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Uniform API

• Provide common access semantics• Map from the interface preferred by your

application to the interfaces required by legacy storage systems

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ArchivesHPSS, ADSM,UniTree, DMF

File SystemsUnix, NT,Mac OSX

AccessAPIs

Servers

PrimeServer

Unix Shell

Java, NTBrowsers

WebWSDL

GridFTP

DatabasesDB2, Oracle,

Postgres

Application

Storage AbstractionCatalog AbstractionDatabases

DB2, Oracle, Sybase

C, C++, Libraries

Logical Name Space

LatencyManagement

DataTransport

MetadataTransport

Consistency Management / Authorization-Authentication

Linux I/O

DLL /Python

SRB and MCATUniform APIs

HRM

Page 19: Storage Resource Broker

Discovery Transparencies

• Naming transparency - find a data set without knowing its name– Map from attributes to a global file name

• Location transparency - access a data set without knowing where it is– Map from global file name to local file name

• Access transparency - access a data set without knowing the type of storage system– Federated client-server architecture

Page 20: Storage Resource Broker

Servers

Unix Shell

Java, NTBrowsers

WebWSDL

GridFTP

ArchivesHPSS, ADSM,UniTree, DMF

DatabasesDB2, Oracle,

Postgres

File SystemsUnix, NT,Mac OSX

Application

HRM

AccessAPIs

Storage AbstractionCatalog Abstraction

DatabasesDB2, Oracle, Sybase

C, C++, Libraries

Logical Name Space

LatencyManagement

DataTransport

MetadataTransport

Consistency Management / Authorization-AuthenticationPrimeServer

Linux I/O

DLL /Python

SRB and MCATTransparencies

Page 21: Storage Resource Broker

Persistent Collection

• Maintain authenticity– Authenticate all accesses– Assign roles for access control lists (curation,

write, annotate, read)– Manage audit trails of all operations

• Collection-owned data– All accesses through the data management

system

Page 22: Storage Resource Broker

AccessAPIs

Servers

PrimeServer

Unix Shell

Java, NTBrowsers

WebWSDL

GridFTP

ArchivesHPSS, ADSM,UniTree, DMF

DatabasesDB2, Oracle,

Postgres

File SystemsUnix, NT,Mac OSX

Application

HRM

Storage AbstractionCatalog Abstraction

DatabasesDB2, Oracle, Sybase

C, C++, Libraries

Logical Name Space

LatencyManagement

DataTransport

MetadataTransport

Consistency Management / Authorization-Authentication

Linux I/O

DLL /Python

SRB and MCATPersistency

Page 23: Storage Resource Broker

• Name transparency– Find a file by attributes (map from attributes to global

name) • Location transparency

– Access a file by a global identifier (map from global to local file name)

• Access transparency– Use same API to access data in archive or file cache

• Authenticity– Disaster recovery, replicate data across storage systems– Audit and process management

Preservation

(Similar requirements to a data grid)

Page 24: Storage Resource Broker

AccessAPIs

Servers

PrimeServer

Unix Shell

Java, NTBrowsers

WebWSDL

GridFTP

ArchivesHPSS, ADSM,UniTree, DMF

DatabasesDB2, Oracle,

Postgres

File SystemsUnix, NT,Mac OSX

Application

HRM

Storage AbstractionCatalog Abstraction

DatabasesDB2, Oracle, Sybase

C, C++, Libraries

Logical Name Space

LatencyManagement

DataTransport

MetadataTransport

Consistency Management / Authorization-Authentication

Linux I/O

DLL /Python

SRB & MCATPreservation

Page 25: Storage Resource Broker

Technology Convergence

• Data grids as basis for distributed data management– Federation of distributed resources– Creation of logical name space to automate discovery

• Distributed data collections– Discovery based on attributes– Distributed data storage systems

• Digital libraries– Development of services for manipulating, viewing data

• Persistent archives– Management of technology evolution

Page 26: Storage Resource Broker

Data Naming Ontologies

Attributes that describe data structure

Data model

Local file nameArchive / file systems

Discipline attributesCollection

Global IdentifierData grid

Discipline conceptsConcept space

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Knowledge Creation

• Knowledge syntax (consensus)

– RDF, XMI, Topic Map• Knowledge management (recursive operations)

– Oracle parallel database• Knowledge manipulation (spatial/procedural rules)

– Generation of inference rules and mapping to data models• Knowledge generation (scalable inference engine)

– Application of inference rules in inference engine

Page 28: Storage Resource Broker

AttributesSemantics

Knowledge

Information

Data

Ingest Services

Management AccessServices

(Model-based Access)

(Data Handling System - SRB)M

CA

T/H

DF

Grid

s

XM

L D

TD

SD

LIP

XTM

DTD

Rul

es -

KQ

L

InformationRepository

Attribute- based Query

Feature-basedQuery

Knowledge orTopic-Based Query / Browse

KnowledgeRepository for Rules

RelationshipsBetweenConcepts

FieldsContainersFolders

Storage(Replicas,Persistent IDs)

Knowledge-based Data Grid

Page 29: Storage Resource Broker

Reagan W. MooreGeneral Atomics

San Diego Supercomputer Center

[email protected]://www.nirvanastorage.com


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