Rule-Based Data Management Rule-Based Data Management SystemsSystems

Reagan W. MooreReagan W. Moore

Wayne SchroederWayne Schroeder

Mike WanMike Wan

Arcot RajasekarArcot Rajasekar

{moore, schroede, mwan, sekar}@sdsc.edu

http://www.sdsc.edu/srb

http://irods.sdsc.edu/http://irods.sdsc.edu/

TopicsTopics

• Managing distributed shared collections• Data grids

• Control of name spaces - SRB• Production system• Data and trust virtualization• Infrastructure independence

• Control of management policies - iRODS• Next generation technology• Management virtualization• Rules controlling remote operations• Constraints on the rules and remote operations

Data Management ApplicationsData Management Applications

• Data grids • Share data

• Digital libraries • Publish data

• Persistent archives • Preserve data

• Real-time sensor streams • Data federation

• Data analysis• Automate access to distributed data

ConceptsConcepts

• Distributed Data Management Concepts• Data virtualization

• Manage the properties of a shared collection independently of the storage systems

• Trust virtualization• Administrative domain independence

• Federation• Managing interactions between data grids

• Rule-based Data Management• Policy virtualization

• Automating execution of management policies• Applying management policies to remote operations

Data GridData Grid

Using a Data Grid – Using a Data Grid – in Abstractin Abstract

Ask for d

ata

•User asks for data from the data grid

Data d

elivere

d

•The data is found and returned•Where & how details are hidden

Using a Data Grid - Using a Data Grid - DetailsDetails

Storage Resource Broker Server

•Data request goes to SRB Server

Storage Resource Broker Server

Metadata Catalog

DB

•Server looks up information in catalog

•Catalog tells which SRB server has data

•1st server asks 2nd for data

•The data is found and returned

•User asks for data

Data VirtualizationData Virtualization

• Manage properties of each digital entity independently of the remote storage systems• Infrastructure independence

• Properties of the shared collection• Name spaces• Persistent state information (location, size,…)

• Manage standard operations• Map from client requests to standard operations• Map from standard operations to remote storage system

protocol

Data VirtualizationData Virtualization

Storage Repository

• Storage location

• User name

• File name

• File context (creation date,…)

• Access controls

Data Grid

• Logical resource name space

• Logical user name space

• Logical file name space

• Logical context (metadata)

• Access constraints

Data Collection

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

Data is organized as a shared collection

Data VirtualizationData Virtualization

Storage SystemStorage System

Storage ProtocolStorage Protocol

Access InterfaceAccess Interface

Standard Access ActionsStandard Access Actions

Data GridData Grid

Map from the Map from the

actions requested byactions requested by

the access methodthe access method

to a standard set ofto a standard set of

micro-services used micro-services used

to interact with theto interact with the

storage systemstorage system

Standard Micro-servicesStandard Micro-services

Standard OperationsStandard Operations

• File manipulation• Posix I/O calls - open, close, read, write, seek, …• Register, replicate, checksum, synchronize

• Bulk operations• Bulk data transport, metadata load• Parallel I/O streams

• Remote procedures• Data filtering, subsetting, metadata extraction• Remote library execution (HDFv5, DataCutter)

BaBar High-Energy PhysicsBaBar High-Energy Physics

• Stanford Linear Accelerator

• IN2P3• Lyon, France• Rome, Italy• San Diego• RAL, UK

• A functioning international Data Grid for high-energy physics

Manchester-SDSC mirror

Moved over 300 TBs of dataMoved over 300 TBs of data

Increasing to 5 TBs per dayIncreasing to 5 TBs per day

Next Generation TechnologyNext Generation Technology

• Every fault that occurs in the distributed environment is the responsibility of the data grid• Network outage / system crash / operator error• Minimize risk through checksums, replicas,

synchronization, federation

• Management of large collections is labor intensive• Initiation of recovery operations after remote system

failure

• Need to automate execution of management policies

Controlling Remote OperationsControlling Remote Operations

Data ManagementEnvironment

ConservedProperties

ControlMechanisms

RemoteOperations

ManagementFunctions

AssessmentCriteria

ManagementPolicies

Capabilities

Data ManagementInfrastructure

PersistentState

Rules Micro-services

PhysicalInfrastructure

Database Rule Engine StorageSystem

iRODS - integrated Rule-Oriented Data SystemiRODS - integrated Rule-Oriented Data System

Support unique organizational / social Support unique organizational / social

management policies for each collectionmanagement policies for each collection

Rule-based Data ManagementRule-based Data Management

• Express assessment criteria through sets of required persistent state information

• Express management policies as sets of rules controlling the execution of micro-services

• Express capabilities as sets of micro-services• Manage persistent state information resulting from

the application of rules controlling execution of remote micro-services

Management VirtualizationManagement Virtualization

• Examples of management policies• Integrity

• Validation of checksums• Synchronization of replicas• Data distribution• Data retention• Access controls

• Authenticity• Chain of custody - audit trails• Track required preservation metadata - templates• Generation of Archival Information Packages

Rule-based Data ManagementRule-based Data Management

• Rules required for standard operations• Posix I/O control• Standard SRB operations

• Administrator controlled rules to implement management policies• Administrative - adding / deleting users, resources• Data ingestion - pre-processing, post-processing• Data transport / deletion - parallel I/O streams, disposition

• User-defined rules, create your own server-side workflow• Rule set for a particular collection, particular user group,

particular storage system, particular micro-service

iRODS RuleiRODS Rule

• Each rule defines • Event• Condition• Action sets (micro-services and rules)• Recovery sets

• Rule types• Atomic, applied immediately• Deferred, support deferred consistent constraints• Periodic, typically used to validate assertions

Rule-based AccessRule-based Access

• Associate security policies with each digital entity• Redaction, access controls on structures within a file• Time-dependent access controls (how long to hold

data proprietary)

• Associate access controls with each rule• Restrict ability to modify, apply rules

• Associate access controls with each micro-service• Explicit control of operation execution within a given

collection• Much finer control than provided by Unix r:w:e

Federation Between Data GridsFederation Between Data Grids

Data Grid

• Logical resource name space

• Logical user name space

• Logical file name space

• Logical rule name space

• Logical micro-service name

• Logical persistent state

Data Collection B

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

Data Grid

• Logical resource name space

• Logical user name space

• Logical file name space

• Logical rule name space

• Logical micro-service name

• Logical persistent state

Data Collection A

Rule-based FederationRule-based Federation

• When registering a digital entity into another data grid, register required management rules along with the digital entity• Move management policies with data

• Expectation that each operation on each digital entity can be controlled across federated data grids• Example is end-to-end encryption

Evolution of Rule-based SystemsEvolution of Rule-based Systems

• Logical name spaces enable dynamic addition of new rules, micro-services, and state information• Apply new rules on one collection while applying old

rule sets on a legacy collection• Can run old and new rule sets in parallel

• Can build a system that manages its evolution• Can create rules that track the evolution of the rule-

based system• Can create rules that govern migration to new rule

sets

Assessment RulesAssessment Rules

• Can build a system that monitors its own state information• Parse audit trails to verify accesses by

authorized persons• Parse persistent state information for compliance

with management rules• Test micro-services for compliance with rules• Audit all accesses to a collection • Compare system properties to desired outcomes

For More InformationFor More Information

Reagan W. Moore

San Diego Supercomputer Center

moore@sdsc.edu

SRB: http://www.sdsc.edu/srb/

iRODS: http://irods.sdsc.edu/