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Domain Specific Software Architectures for Science Lecture for Software Architectures USC 578

Domain Specific Software Architectures for Science Lecture for Software Architectures USC 578 Dan Crichton April 2010 Topics Introduction – who am I? Architecture – what is means to me Challenges in Developing Architectures Reference Architecture vs Domain Specific Software Architectures

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Domain Specific Software Architectures for Science Lecture for Software Architectures USC 578

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  1. Domain Specific Software Architectures for ScienceLecture for Software Architectures USC 578 Dan Crichton April 2010

  2. Topics • Introduction – who am I? • Architecture – what is means to me • Challenges in Developing Architectures • Reference Architecture vs Domain Specific Software Architectures • Experience in Science • Lessons Learned • Q&A

  3. 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

  4. Architecture: why do I care? • Architecture is a game changer in our business • Enable scientific discovery, novel engineering, etc • Coordination across multiple enterprises • Data system costs per mission, project, investigation, etc is high • Technology infusion is limited • Experience and knowledge reuse

  5. But, there are challenges • Lack 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

  6. 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

  7. 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)

  8. Communicating an architecture • A 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 stakeholders • Different 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. The view is what you see The viewpoint is where you look from Viewpoints and views • 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 (Project Managers, Engineers, Scientists, Business Analysts, …)

  10. Reference Architectures • Show components, functions, and interfaces at a high level of abstractions • Likewise, 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 model • Implementation 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

  11. 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 space • Reference 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

  12. RAs vs DSSAs in Science • In science data systems, construction of multiple architecture viewpoints of a system is critical • Process/Enterprise • Information/Data • Technology • We find the “viewpoints” are similar, but models can be domain specific • This is the opportunity to develop a reusable reference architecture if the “patterns” can be extracted

  13. Scientific data systems • Covers a wide variety of disciplines • Solar system exploration • Astrophysics • Earth science • Biomedicine • etc • Each has its own communities, standards and systems • But, there is an underlying reference architecture and discipline software architectures in each!

  14. The “e-science” trend • Highly 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

  15. Context: Space data systems Relay Satellite Simple Information Object Spacecraft and Scientific Instruments Spacecraft / lander Science Data Archive External Science Community Primitive Information Object Primitive Information Object Science Information Package Science Information Package Science Data Processing Science Products - Information Objects Telemetry Information Package Science Information Package Data Analysis and Modeling Science Information Package Planning Information Object Instrument Planning Information Object Science Team Data Acquisition and Command Mission Operations Instrument /Sensor Operations • Common Meta Models for Describing Space Information Objects • Common Data Dictionary end-to-end

  16. Science Processing Center 1 Archive & Distribution (DAAC 1) Earth Science Data Systems DS Mission #1 PO.DAAC Science Processing Center 2 Archive & Distribution (DAAC 2) Distributed Data Analysis (Subsetting, Gridding, Transformation,Modeling) DS Mission #2 Users Other Data Sources (e.g. NOAA) SMAP, Desdyni Infrastructure to support Analysis of Distributed Data

  17. Cancer research

  18. Patterns in scientific data systems • Instrument and Spacecraft Commands • Instruments that capture observations • Generation of Engineering and Science Data Products • Data Processing • Data Management • Data Distribution • Distributed Facilities • Data Movement

  19. Finding the reference architecture • Simple SOA-style pattern • Data/Information Architecture • Components, middleware, and communication • NOTE: Process is implicit here

  20. Usability Diversity within the domain Scalability Reliability Portability NOTE: Our reference architecture must address these ilities long term “Ilities” in science data systems

  21. Specialization within domains • Domain information models • Planetary Science Ontology • Cancer Biomarker Ontology • Etc • Specific services and domain implementations are derived from the reference architecture • Reference Architecture->Domain Specific Software Architecture-> Domain Implementations • In these science domains, the architectures need to be long-lived (20+ years)

  22. Derived Planetary Data System Architecture

  23. Software product lines • This is about strategy more than technology • Goal is a software product line that • Implements our reference architecture • Allows 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

  24. 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

  25. Architectural principles* • Separate the technology and the information architecture • Encapsulate the messaging layer to support different messaging implementations • Encapsulate individual data systems to hide uniqueness • Provide data system location independence • Require that communication between distributed systems use metadata • Define a model for describing systems and their resources • Provide scalability in linking both number of nodes and size of data sets • Allow systems using different data dictionaries and metadata implementations to be integrated • Leverage existing software, where possible (e.g., open source, etc)` * 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.

  26. Architectural focus • Consistent 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

  27. Exploiting common patterns • How 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…

  28. 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

  29. 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

  30. nasa.pds.xmlquery XMLQuery XMLQuery 1 fromSet - - resultModeId: String resultModeId: String - - propogationType: String propogationType: String QueryElement QueryElement selectSet 1 - - propogationLevels: String propogationLevels: String - - role: String role: String - - maxResults: int maxResults: int whereSet 1 - - value: String value: String - - kwqString: String kwqString: String - - numResults: int numResults: int - - mimeAccept: List mimeAccept: List 1 result 1 queryHeader 1 QueryHeader QueryHeader - - id: String id: String QueryResult QueryResult 1 - - title: String title: String - - list: List list: List - - description: String description: String - - type: String type: String - - statusID: String statusID: String - - securityType: String securityType: String - - revisionNote: String revisionNote: String - - dataDictID: String dataDictID: String OODT data architecture models Based on Dublin Core Request/Response Model Resource Metadata Model Based on ISO/IEC 11179

  31. OODT software components • Profile 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.

  32. Distributed architecture 1. Science data tools and applications use “APIs” to connect to a virtual data repository 2. Middleware creates the data grid infrastructure connecting distributed heterogeneous systems and data 3. Repositories for storing and retrieving many types of data Mission Data Repositories OODT Reusable Data Grid Framework OODT API Visualization Tools Biomedical Data Repositories OODT API Web Search Tools Engineering Data Repositories OODT API Analysis Tools

  33. Technology architecture Service Registry Name Server Name Server Registry Server Node 1 Profile Server WSDL WSDL Web I/F Node 1 Profile Server Query Integration Node 1 Profile Server XML Request Information Object Product Catalogs XML Request Repository Product Server XML Request Desktop I/F Information Object Information Object Science Products XML Request Repository Product Server Info Object Information Object Science Products … XML Request Repository/Archive Server • Common Meta Models for Describing Space Information Objects • Common Data Dictionary end-to-end Science Products

  34. 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

  35. 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

  36. Deployed EDRN System

  37. Application to planetary science • 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

  38. Earth Science Data Systems Other Data Systems Web Portal Distributed Data Analysis Airborne Instruments Data Production/Processing Data Integration Data Acquisition/Ingestion Catalogs Multi-mission Policies & Rules Local Storage (Models, Data, etc) (Testbed and Operational Deployed Environments) Surface Instruments Special Product Processing Environment / Computational Infra Modeling and Visualization Facility

  39. Application to Climate Research • Highly distributed modeling and observational systems • Heterogeneous implementations • Different purposes • But, brought together as a virtual system, provides new science discovery opportunities (Models) (Observations)

  40. NASA & Earth System Grid

  41. Lessons Learned • A reference architecture is critical for driving a strategy and support large-scale/enterprise systems • However, limited experience in organizations to build reference architectures • Useful 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

  42. 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 architecture • My 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 time • This is why an architect is much more of a strategist than a technologist

  43. Final Thoughts • Software architecture in science is critical to • Reducing cost of building science data systems • Building virtual organizations • Constructing software product lines • Driving standards • Supporting 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!

  44. 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.

  45. Backup

  46. 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 EDRN CDE Tools EDRN Biomarker Ontology Model

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