dan crichton april 2010. topics introduction – who am i? architecture – what is means to me...
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Dan Crichton
April 2010
TopicsIntroduction – who am I?Architecture – what is means to meChallenges in Developing ArchitecturesReference Architecture vs Domain Specific
Software ArchitecturesExperience in ScienceLessons LearnedQ&A
Who am I?Employed by Jet Propulsion Laboratory since 1995; prior software
engineering positions at Hughes Aircraft Company and in private industry
MS in Computer Science, USC; 20+ years of experience
Program Manager & Principal Computer Scientist for Planetary Data System Engineering in Solar System Exploration Directorate Data Systems and Technology in Earth and Technology Directorate
Principal Investigator for Informatics Center, Early Detection Research Network, National Cancer Institute Facilitating Integration of NASA and Earth System Grid, NASA Object Oriented Data Technology
Several co-Investigator Tasks
Architecture: why do I care?Architecture is a game changer in our business
Enable scientific discovery, novel engineering, etcCoordination across multiple enterprises
Data system costs per mission, project, investigation, etc is high
Technology infusion is limited
Experience and knowledge reuse
But, there are challengesLack of true architects
Most think of point solutions or confuse architecture and implementation
Abstracting is difficult
Governance is often at a project level; little view at an enterprise level
Limited planning and understanding of the reference requirements
Architects: what are they?Effective Architects have…
• Years of experience
• Holistic view of domain – Look at both aesthetics and
practical details– Variable technical depth
• Lifecycle roles– Strong involvement up-front– May oversee development– Chooses stable steps in
development
Effective Architects are not…
• Lone inventors or scientists– The architect is a good
communicator and politician -- architectures must be sold and explained and their integrity maintained
– Architecting is not a science, but depends on science
• Purely technologists• Architecture is a strategy
• “Top level only” designers– Details are often critical
• Collaborators– A coherent vision is critical;
they drive it
Architecture: what is it?The fundamental organization of a system
embodied in its components, their relationships to each other, and to the environment, and the principles guiding its design and evolution. (ANSI/IEEE Std. 1471-2000)
Communicating an architectureA good architecture is one that can be
communicated to the stakeholders
A good architecture presents viewpoints of the system that address stakeholder concerns
A good architecture uses models and descriptions that are relevant to the stakeholdersDifferent models may be used to present different
viewpoints (e.g., A UML model of the system may be appropriate for some but not all stakeholders)
9
• A viewpoint is a template for constructing a view• Enterprise, Functional,
Informational, etc• A view is a description of
the entire system from the perspective of a set of related concerns. A view is composed of one or more models.
• A model is an abstraction or representation of some aspect of a thing
• Examples: RM-ODP, FEAF, TOGAF, etc
The viewpoint is where you look from
The view is what you see
(Project Managers, Engineers, Scientists, Business Analysts, …)
Reference ArchitecturesShow components, functions, and interfaces at a high
level of abstractionsLikewise, we consider information models to also be
part of a reference architecture (at a sufficient abstract level)In observing systems, the information model patterns
are highly compatible as a reference information modelImplementation neutral; architectural frameworks
can be useful in defining a structure for a reference architecture
We use Reference Architectures to give us a strategic advantage as well as improve enterprise scale software
Domain Specific Software Architectures*Domain model
Leverage experts who have the “holistic” view and can drive the need for product lines
An unambiguous view is critical (in fact, this has been a problem in science arenas)
Reference requirements Drives the reference architecture However, it is critical to map domain models to reference requirements
in order to understand the solution spaceReference architecture
Satisfies an abstracted set of functions from the reference requirements
It’s engineered for the “ilities” reusability, extensibility and configurability
It demonstrates the separation of functional elements of the architecture
* Tracz, Will, Domain-Specific Software Architecture, ACM SIGSOFT, 1995
RAs vs DSSAs in Science
In science data systems, construction of multiple architecture viewpoints of a system is criticalProcess/EnterpriseInformation/DataTechnology
We find the “viewpoints” are similar, but models can be domain specificThis is the opportunity to develop a reusable
reference architecture if the “patterns” can be extracted
Scientific data systemsCovers a wide variety of disciplines
Solar system exploration AstrophysicsEarth scienceBiomedicineetc
Each has its own communities, standards and systems
But, there is an underlying reference architecture and discipline software architectures in each!
The “e-science” trendHighly distributed, multi-organizational systems
Systems are moving towards loosely coupled systems or federations in order to solve science problems which span center and institutional environments
Sharing of data and services which allow for the discovery, access, and transformation of data Systems are moving towards publishing of services and data in order to address
data and computationally-intensive problems Infrastructures which are being built to handle future demand
Address complex modeling, inter-disciplinary science and decision support needs Need a dynamic environment where data and services can be used quickly as the
building blocks for constructing predictive models and answering critical science questions
Changing the way in which data analysis is performed Moving towards analysis of distributed data to increase the study power Enabling greater collaboration across centers
DJC-15
External Science
Community
Data Acquisition
and CommandMission
OperationsInstrument /Sensor Operations
ScienceData
Archive
ScienceData
Processing
Data Analysis and
Modeling
Science Information Package
Science Team
Relay Satellite
Spacecraft / lander
Spacecraft andScientific Instruments
Primitive Information Object
Primitive Information Object
Simple Information Object
Telemetry Information Package
Science Information Package
Instrument Planning
Information Object
Science Information Package
Science Products - Information Objects
PlanningInformation
Object
Science Information Package
• Common Meta Models for Describing Space Information Objects• Common Data Dictionary end-to-end
Science Processing Center
1
Science Processing Center
2
Archive & Distributi
on(DAAC 1)
Archive & Distributi
on(DAAC 2)
Distributed Data Analysis(Subsetting,
Gridding,Transformation,Modeling)
Other Data
Sources (e.g.
NOAA)
DS Mission #1
DS Mission #2 Users
SMAP, Desdyni
PO.DAAC
Infrastructure to supportAnalysis of Distributed Data
Patterns in scientific data systemsInstrument and Spacecraft CommandsInstruments that capture observationsGeneration of Engineering and Science Data
ProductsData ProcessingData ManagementData DistributionDistributed FacilitiesData Movement
• Simple SOA-style pattern
• Data/Information Architecture
• Components, middleware, and communication
• NOTE: Process is implicit here
Middleware andMessaging
Comm Layer
Metamodel
InformationComponents
InformationObject
Domain Model
Metamodel
InformationComponents
InformationObject
Domain Model
Middleware andMessaging
Comm LayerCommon Protocols - TCPIP, ...
Common Messaging - SOAP, JMS, ...
Common Functions - Registry, Repository, ...
Common or Mediated Metamodel - DEDSL,ISO1179, UML
Common or Mediated Domain Models --Planetary Data Systems, EOSDIS, ...
Information Exchange - Science, Mission, etc, DataProducts, Observations, SLE Objects, ...
Communications
Software/Application
DataArchitecture/Content
DJC-20
Usability
Diversity within the domain
Scalability
Reliability
Portability
NOTE: Our reference architecture must address these ilities long term
Cumulative Volume of L2+ Products at All DAACs
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
FY00 FY01 FY02 FY03 FY04 FY05 FY06 FY07 FY08 FY09 FY10 FY11 FY12 FY13 FY14
Fiscal Year
Cumulative Volume (TB)
Specialization within domainsDomain information models
Planetary Science OntologyCancer Biomarker OntologyEtc
Specific services and domain implementations are derived from the reference architectureReference Architecture->Domain Specific Software
Architecture-> Domain Implementations
In these science domains, the architectures need to be long-lived (20+ years)
Software product linesThis is about strategy more than technology
Goal is a software product line thatImplements our reference architectureAllows for construction of core software
components that can be reused across projects and science disciplines
Can demonstrate sufficient cost and schedule benefits without sacrificing flexibility in meeting requirements and adapting to technology change
Extensions can be applied at the discipline level
Object Oriented Data Technology• Represents both a reference
architecture AND a software product line for science data systems
• Exploits common patterns• Delivers reusable software
components as building blocks for construction of higher order data systems
• Applied to multiple science disciplines
• Funded originally back in 1998; runner up for NASA Software of the Year in 2003
• Heavily used by NASA and NIH projects
OODT/Science Web Tools
OODT/Science Web Tools
ArchiveClient
OBJ ECT ORIENTED DATA TECHNOLOGY FRAMEWORK
ProfileXMLData
ProfileXMLData
NavigationService
NavigationService
Data System
2
Data System
2
Data System
1
Data System
1
Other Service 1
Other Service 1
Other Service 2
Other Service 2
QueryServiceQuery
ServiceProductServiceProductService
ProfileServiceProfileService
ArchiveServiceArchiveService
Bridge to External Services
Bridge to External Services
DJC-24
Architectural principles*Separate the technology and the information architectureEncapsulate the messaging layer to support different messaging
implementationsEncapsulate individual data systems to hide uniquenessProvide data system location independence Require that communication between distributed systems use
metadataDefine a model for describing systems and their resources Provide scalability in linking both number of nodes and size of data
setsAllow systems using different data dictionaries and metadata
implementations to be integratedLeverage existing software, where possible (e.g., open source, etc)`
DJC-25
* Crichton, D, Hughes, J. S, Hyon, J, Kelly, S. “Science Search and Retrieval using XML”,Proceedings of the 2nd National Conference on Scientific and Technical Data, National Academy of Science, Washington DC, 2000.
Architectural focusConsistent distributed capabilities
Resource discovery (data, metadata, services, etc), “grid-ing” loosely coupled science system, workflow management
On-demand, shared services (E.g. processing, translation, etc) Processing Translation
Deploy high throughput data movement mechanisms
End-to-end capabilities across the science environment
Reduce local software solutions that do not scale Increasing importance in developing an “enterprise” approach with
common services
Build value-added services and capabilities on top of the infrastructure
DJC-26
Exploiting common patternsHow data is managed (registry/repository,
information objects themselves)…How data is generated, captured, etc (e.g.,
workflow and data processing)…How data is accessed (metadata, data)…How information is discovered …How data is distributed (e.g., transformed)…How data is visualized…
What does OODT do? Tie together loosely coupled distributed heterogeneous data
systems into a virtual data grid
Support critical functions Data Production and workflow Data Distribution Data Discovery (including query optimization across highly distributed
systems) Data Access
An architectural approach first, an implementation second Adapt to different distributed computing deployments Promotes a REST-style architectural pattern for search and retrieval
Scalability in linking together large, distributed data sets
OODT data architecture focus
On types of and relationships among a software system’s data
Decomposition of data within a software system to its logical components and interactions
Components: Data Elements, Data Dictionary, Data Models of individual data sources
Interactions: Mappings between Data Dictionary to Data Models, Data Element structural comparison
Some standards currently exist for data architecture ISO: ISO-11179 Standardization and Specification of Data Elements Dublin Core Metadata Initiative: Dublin Core Data Elements to describe any
electronic resource
Specifications for the Data Architecture Common XML schema for managing information about data
resources Common XML schema for messaging between distributed services Methods for integrating existing domain models within architecture
ProfileAttributes
-id: String-version: String-statusID: String-securityType: String-parent: String-children: List-regAuthority: String-revisionNotes: List-dataDictID: String
ProfileAttributes
-id: String-version: String-statusID: String-securityType: String-parent: String-children: List-regAuthority: String-revisionNotes: List-dataDictID: String
ResourceAttributes
-identifier: String-title: String-formats: List-description: String-creators: List-subjects: List-publishers: List-contributors: List-dates: List-sources: List-languages: List-coverages: List-rights: List-contexts: List-aggregation: String-clazz: String-locations: List
ResourceAttributes
-identifier: String-title: String-formats: List-description: String-creators: List-subjects: List-publishers: List-contributors: List-dates: List-sources: List-languages: List-coverages: List-rights: List-contexts: List-aggregation: String-clazz: String-locations: List
ProfileElement
-name: String-id: String-desc: String-type: String-unit: String-synonyms: List-obligation: boolean-maxOccurrence: int-comments: String
ProfileElement
-name: String-id: String-desc: String-type: String-unit: String-synonyms: List-obligation: boolean-maxOccurrence: int-comments: String
EnumeratedProfileElement
-values: List
EnumeratedProfileElement
-values: List
RangedProfileElement
-min: double-max: double
RangedProfileElement
-min: double-max: double
ProfileProfile
UnspecifiedProfileElement
UnspecifiedProfileElement
MapMap
resourceAttributesprofileAttributes
elements1 1
1
1 11
*
profile profile
Keys areStrings,equal toelements’names
Resource Metadata Model
Request/Response Model
Based on ISO/IEC 11179
Based on Dublin Core
XMLQuery
-resultModeId: String-propogationType: String-propogationLevels: String-maxResults: int-kwqString: String-numResults: int-mimeAccept: List
XMLQuery
-resultModeId: String-propogationType: String-propogationLevels: String-maxResults: int-kwqString: String-numResults: int-mimeAccept: List
QueryHeader
-id: String-title: String-description: String-type: String-statusID: String-securityType: String-revisionNote: String-dataDictID: String
QueryHeader
-id: String-title: String-description: String-type: String-statusID: String-securityType: String-revisionNote: String-dataDictID: String
QueryResult
-list: List
QueryResult
-list: List
QueryElement
-role: String-value: String
QueryElement
-role: String-value: String
1
1
1
1
1
1
1
fromSet
selectSet
whereSet
resultqueryHeader
nasa.pds.xmlquery
OODT software componentsProfile Service – A server-based registry that is
able to either serve local XML profiles or plug-into an existing catalog. This component provides resource discovery.
Product Service – A server component that plugs into existing repositories and serves products. This includes translation serves, etc
Catalog and Archive Service – Transaction-based server that catalogs and archives products providing profile and product servers for discovery and distribution
Query Service – Provides query management across distributed services to enable discovery.
DJC-32
3. Repositories for storing and retrieving many types of data
1. Science data tools and applications use “APIs” to connect to a virtual data repository
Visualization Tools
Analysis Tools
OODTReusable
DataGrid
Framework
OODTReusable
DataGrid
Framework
MissionData
Repositories
MissionData
RepositoriesOODT
API
OODTAPI
2. Middleware creates thedata grid infrastructure connecting distributed heterogeneous systems and data
BiomedicalData
Repositories
BiomedicalData
Repositories
EngineeringData
Repositories
EngineeringData
Repositories
Web Search Tools
OODTAPI
OODTAPI
OODTAPI
OODTAPI
• Common Meta Models for Describing Space Information Objects• Common Data Dictionary end-to-end
Query Integration
Node 1Profile Server
XML Request
Information Object
XML Request
Info
Ob
ject
XM
L R
eque
st
Repository Product Server
Information Object
Web I/F
Desktop I/F
XML Request
Information Object
Name Server
Repository Product Server
Node 1Profile Server
Node 1Profile Server
Registry Server
Repository/ArchiveServer
…
Name ServerService Registry
XML Request
Information Object
WSDL WSDL
ProductCatalogs
Science Products
ScienceProducts
Science Products
OODT software implementation OODT is Open Source Developed using open source software (i.e. Java/J2EE and XML) Implemented reusable, extensible Java-based software components
Core software for building and connecting data management systems Provided messaging as a “plug-in” component that can be replaced
independent of the other core components. Messaging components include: CORBA, Java RMI, JXTA, Web Services, etc REST seems to have prevailed
Provided client APIs in Java, C++, HTTP, Python, IDL Simple installation on a variety of platforms (Windows, Unix, Mac OS X,
etc) Used international data architecture standards
ISO/IEC 11179 – Specification and Standardization of Data Elements Dublin Core Metadata Initiative W3C’s Resource Description Framework (RDF) from Semantic Web Community
DJC-34
EDRN Knowledge Environment EDRN has been a pioneer in the use of
informatics technologies to support biomarker research
EDRN has developed a comprehensive infrastructure to support biomarker data management across EDRN’s distributed cancer centers
Twelve institutions are sharing data Same architectural framework as planetary
science
It supports capture and access to a diverse set of information and results
Biomarkers Proteomics Biospecimens Various technologies and data products
(image, micro-satellite, …) Study Management
DJC-35
DJC-37
• Often unique, one of a kind missions– Can drive technological changes
• Instruments are competed and developed by academic, industry and industrial partners
– Highly distributed acquisition and processing across partner organizations
– Highly diverse data sets given heterogeneity of the instruments and the targets (i.e. solar system)
• Missions are required to share science data results with the research community requiring:
– Common domain information model used to drive system implementations
– Expert scientific help to the user community on using the data
– Peer-review of data results to ensure quality– Distribution of data to the community
• Planetary science data from NASA (and some international) missions is deposited into the Planetary Data System
Planetary Data SystemDistributed Planetary Science Archive
Small Bodies NodeUniversity of Maryland
College Park, MD
Planetary Plasma Interactions NodeUniversity of California Los AngelesLos Angeles, CA
Geosciences NodeWashington University
St. Louis, MOImaging NodeJPL and USGSPasadena, CA and Flagstaff, AZ
THEMIS Data NodeArizona State UniversityTempe, AZ
Central NodeJet Propulsion LaboratoryPasadena, CA
Navigation Ancillary Information NodeJet Propulsion LaboratoryPasadena, CA
Rings NodeAmes Research CenterMoffett Field, CA
Atmospheres NodeNew Mexico State UniversityLas Cruces, NM
Other Data
Systems
CatalogsCatalogs
Distributed Data Analysis
AirborneInstruments
Local Storage(Models, Data, etc)Local Storage
(Models, Data, etc)
Multi-missionPolicies &
Rules
Multi-missionPolicies &
Rules
Data Acquisition/Inges
tion
Special ProductProcessing Environment /
Computational Infra
Web Portal
Data Production/Proce
ssing
Data Integration
Modelingand VisualizationFacility
Surface Instruments
(Testbed and Operational
DeployedEnvironments)
Application to Climate Research
Highly distributed modeling and observational systems
Heterogeneous implementations
Different purposesBut, brought together
as a virtual system, provides new science discovery opportunities (Observations) (Models)
Lessons LearnedA reference architecture is critical for driving a
strategy and support large-scale/enterprise systemsHowever, limited experience in organizations to build
reference architecturesUseful ways to represent the architecture can be
tough!How detailed to make the reference architecture is
an art! (Don’t let the implementation drive the RA)
Products lines are useful to providing reusable components based on the reference architecture
More Lessons Learned….Distributed service architectures
Not anything new (my experience with them goes back to the early 1990s)
But, often, newer technologies and approaches are seen as a panacea
Technology is not a replacement for a conceptual architectureMy experience is that definition of the architecture
independent of technology is critical The goal should be stability in the architecture model; the
selection of appropriate technology will change over timeThis is why an architect is much more of a strategist than a
technologist
Final ThoughtsSoftware architecture in science is critical to
Reducing cost of building science data systemsBuilding virtual organizationsConstructing software product linesDriving standardsSupporting new paradigms in mission operations and
scientific research
Science is still learning how to best leverage technology in a collaborative discovery environment, but significant progress is being made!
Resources (1) Tracz, Will. Domain-Specific Software Architecture. ACM
SIGSOFT, 1995.
(2) D. Crichton, S. Kelly, C. Mattmann, Q. Xiao, J. S. Hughes, J. Oh, M. Thornquist, D. Johnsey, S. Srivastava, L. Esserman, and B. Bigbee. A Distributed Information Services Architecture to Support Biomarker Discovery in Early Detection of Cancer. In Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing, pp. 44, Amsterdam, the Netherlands, December 4th-6th, 2006.
(3) C. Mattmann, D. Crichton, N. Medvidovic and S. Hughes. A Software Architecture-Based Framework for Highly Distributed and Data Intensive Scientific Applications. In Proceedings of the 28th International Conference on Software Engineering (ICSE06), pp. 721-730, Shanghai, China, May 20th-28th, 2006.
EDRN’s Ontology Model EDRN has developed a High level ontology
model for biomarker research which provides standards for the capture of biomarker information across the enterprise
Specific models are derived from this high level model
Model of biospecimens Model for each class of science data
EDRN is specifically focusing on a granular model for annotating biomarkers, studies and scientific results
EDRN has a set of EDRN Common Data Elements which is used to provide standard data elements and values for the capture and exchange of data
DJC-46EDRN Biomarker Ontology Model
EDRN CDE Tools