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An Intelligent Rule-Oriented Data Management System

DataGrid. An Intelligent Rule-Oriented Data Management System. Wayne Schroeder San Diego Supercomputer Center, University of California San Diego. Talk Outline. Background Brief Overview of the SDSC SRB Current Projects/Usage Activities/Plans Rule-Oriented Data Management System

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An Intelligent Rule-Oriented Data Management System

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  1. DataGrid An Intelligent Rule-Oriented Data Management System Wayne Schroeder San Diego Supercomputer Center, University of California San Diego

  2. Talk Outline • Background • Brief Overview of the SDSC SRB • Current Projects/Usage • Activities/Plans • Rule-Oriented Data Management System • iRODS Requirements/Planning • Architecture • Infrastructure Development • Collaborations/Plans

  3. Data delivered Ask for data • The data is found and returned • Where & how details are hidden Using a Data Grid – in Abstract Data Grid • User asks for data from the data grid

  4. DB Storage Resource Broker Metadata Catalog Storage Resource Broker Using a Data Grid - Details • User asks for data • Data request goes to SRB Server • Server looks up data in catalog • Catalog tells which SRB server has data • 1st server asks 2nd for data • The data is found and returned

  5. Using a Data Grid - Details DB MCAT SRB SRB SRB SRB SRB SRB • Data Grid has arbitrary number of servers • Complexity is hidden from users

  6. Storage Resource Broker A Data Grid Solution • Collaborative client-server system that federates distributed heterogeneous resources using uniform interfaces and metadata • Provides a simple tool to integrate data and metadata handling – attribute-based access • Blends browsing and searching • Developed at SDSC • Operational for 7+ years; • Under continual development since 1997; • Customer-driven

  7. Some SRB Features The SRB is an integrated solution which includes: • a logical namespace, • interfaces to a wide variety of storage systems, • high performance data movement (including parallel I/O), • fault-tolerance and fail-over, • WAN-aware performance enhancements (bulk operations), • storage-system-aware performance enhancements ('containers' to aggregate files), • metadata ingestion and queries (a MetaData Catalog (MCAT)), • user accounts, groups, access control, audit trails, GUI administration tool • data management features, replication • user tools (including a Windows GUI tool (inQ), a set of SRB Unix commands, and Web (mySRB)), and APIs (including C, C++, Java, and Python). SRB Scales Well (many millions of files, terabytes) Supports Multiple Administrative Domains / MCATs (srbZones) And includes SDSC Matrix: SRB-based data grid workflow management system to create, access and manage workflow process pipelines.

  8. SRB Projects • Astronomy • National Virtual Observatory • Data Grids • UK e-Science CCLRC • Teragrid • Digital Libraries and Archives • National Archives and Records Administration • National Science Digital Library • Persistent Archive Testbed • Ecological, Environmental, Oceanographic • ROADnet • Southern California Earthquake Center • SIO Digital Libraries • Molecular Sciences • Synchrotron Data Repository • Alliance for Cellular Signaling • Neuro Sciences • Biomedical Information Research Network • Physics and Chemistry • BaBar • Many others Over 650 Tera Bytes in 106 million files

  9. SRB Scalability • Over 2 Petabytes World-wide • Major SRB instances in the UK, Australia, Taiwan, US • United Kingdom - UK e-Science • Australia - APAC • Taiwan - Academia Sinica, NCHC • Europe -IN2P3, Italy, Norway • United States • 660 Terabytes at SDSC • 100 Million files • SAM QFS, HPSS, Unix file system, SRB Bricks

  10. SDSC Hosted SRB Data

  11. Case Study: SRB in BIRN BIRN Toolkit Collaboration Applications Queries/Results Data Management Viewing/Visualization Mediator GridPort Grid Management Data Model Database Scheduler Database Data Grid Computational Grid NMI SRB Globus MCAT Data Access HPSS File System Distributed Resources

  12. Federated SRB Operation Peer-to-peer Brokering Read Application in Boston Parallel Data Access Logical Name Or Attribute Condition 1 6 5/6 SRB server 3 SRB server 4 SRB agent 5 SRB agent Durham 2 San Diego Server(s) Spawning R2 R1 MCAT 1.Logical-to-Physical mapping 2. Identification of Replicas 3.Access & Audit Control Data Access R2

  13. Application Resource, User Java, NT Browsers Prolog Predicate C, C++, Linux I/O Unix Shell Third-party copy Web User Defined SRB Remote Proxies MCAT Databases DB2, Oracle, Sybase Archives HPSS, ADSM, UniTree, DMF File Systems Unix, NT, Mac OSX HRM Dublin Core DataCutter Application Meta-data SDSC Storage Resource Broker & Meta-data Catalog

  14. IRODS - the Next Generation of Data Grid Technology

  15. MCAT1 Server1.1 Server1.2 MCAT3 Server3.1 MCAT2 Server2.2 Server2.1 Moving Forward, a Two-Prong Plan • Maintain and Adapt SRB to New Usages: • SRB has reached a Stable Plateau • Bug Fixes • Some New Features • Merge Features Developed by others • Continue Testing • Improve Documentation • Continue Application Support • Existing and new Projects • Continue Answering User Queries • Chart New Areas • Federation Research - ZoneSRB • Collaborative Data Grids • Real-time Data Grids - • Virtual Object Ring Buffer • Sensors and Video Streams • Collaborating Observatories • SRB Workflows - New UI for Admins and users • Kepler actors, Matrix, etc • iRODS - Adaptive Middleware Architecture

  16. Continuing SRB Support • 10 FTEs SRB • 5 FTEs iRODS • iRODS Developers Support SRB

  17. Next generation Data Architecture • SRB is quite complex – with too many functions and operations • The intelligence is hard-coded • extensions/modifications require extreme care • But, the modules are fairly robust and reusable • AIM: Can we make SRB more flexible • Easy to customize at finer level • Example: Higher authentication for a particular collection • Example: Can we use stricter authorization for a collection • Example: Can we treat a particular resource differently • Currently- needs code changes • Solution: Use rule-based architecture to provide flexibility

  18. iRODS • A New Paradigm in Middleware Development • Flexible Collection management • Can be customized at user/collection-levels, … • Language for Collection management • As in stored procedures, triggers (RDB) • Administrative ease • Lot of potential beyond SRB • adaptive middleware architectures • This will be a fully Open Source effort

  19. Client Interface Admin Interface Rule Invoker Service Manager Rule Modifier Module Config Modifier Module Metadata Modifier Module Resource-based Services Rule Consistency Check Module Consistency Check Module Consistency Check Module Micro Service Modules Engine Current State Confs Metadata-based Services Rule Base Meta Data Base Micro Service Modules Rule-Oriented Data Systems Framework Resources

  20. Client Operation such as srbObjCreate Server-side Client-side Rule Checking Condition checking, rule firing Setup state and interact with RCAT – updates and modifications to persistent state Establish State Data Movement Backend Processing Micro Services Cleanup state and interact with RCAT – updates and modifications to persistent state CleanUp Rule-oriented Data System (Phase I Operational Model)

  21. Rules and Constraints • Rule-based • Lower-level Functions are composed of micro-services • Higher-level Functions are composed of rules of lower-level micro-services • Rules are interpreted using a rule engine • Customizability • Problems with rule composition • Integrity checks to make sure rules do not break higher-level functionalities • Declarative programming • Rules define semantics • Operational programming • Rule invocation provides procedural interpretation • Rules can be used as “checks and balances” to make sure that collections are self-consistent • Example: Rule makes two copies of each files • Constraint checking: can be used to see if the collection is consistent with this rule

  22. Rule Scalability and Decidability • Distinct Sets of Rules Applied in Different Ways • Atomic • Deferred (state flags) • Compound • Applied Using Micro-services • Granularity • User Input to Influence Rule Expression • Administration Enforcement • Collection Consistency Management • Rule Properties • Metadata Managing Execution (granularity, periodicity) • Metadata Defining Result of Rule Execution

  23. Sample Rules chkCond1(S) :- user(S) == ‘adil@cclrc’. chkCond1(S) :- coll(S) like ‘*/scec.sdsc/img/*’. chkCond2(S) :- user(S) == ‘*@nara’. chkCond3(S) :- user(S) == ‘@salk’. chkCond4(S) :- user(S) == ‘@birn’ , datatype(S) == ‘DICOM’. [OprList] implies delay for later or send to a CronJobManager Opr||Opr implies do them in parallel Opr, Opr implies do them serially • ingestInCollection(S) :- /* store & backup */ • chkCond1(S), ingest(S), register(S) • findBackUpRsrc(S.Coll, R), replicate(S,R). • ingestInCollection(S) :- /*store & check */ • chkCond2(S),computeClntChkSum(S,C1), • ingest(S), register(S), • computeSerChkSum(S,C2), • checkAndRegisterChkSum(C1,C2,S). • ingestInCollection(S) :- /* store, chk, backup & chk */ • chkCond3(S),computeClntChkSum(S,C1), • ingest(S), register(S), • computeSerChkSum(S,C2), • checkAndRegisterChkSum(C1,C2,S), • findBackUpRsrc(S.Coll, R), replicate(S,R) • computeSerChkSum(S,C3), checkAndRegisterChkSum(C2,C3,S). • ingestInCollection(S) :- /*store,check, backup & extract metadata */ • chkCond4(S),computeClntChkSum(S,C1), • ingest(S), register(S), • computeSerChkSum(S,C2), • checkAndRegisterChkSum(C1,C2,S), • findBackUpRsrc(S.Coll, R), [replicate(S,R) || extractRegisterMetadata(S)]. • ingestInCollection(S) :- /* just store */ ingest(S), register(S).

  24. New DataGrid Technology • Next Generation SRB -- iRODS: Intelligent Rule-Oriented Data Systems • Customizable and Flexible – User Configurable • Administratively Simpler – Admin Configurable • Build upon the experience of SRB Data Grid • Transition from SRB to iRODS • Client-level similarity • Meta Catalog transition • Current NSF Funding • Information Technology Research • 2 years • ~ 2 FTEs • Simple proto-type in a year • Started September 2004 • Rule-based architecture • Follow-on funding • NARA • NSF

  25. iRODS Collaborations • SRB/iRODS Developers • Arcot Rajasekar • Michael Wan • Wayne Schroeder • Other SRB Team Members • Collaborative Development • UK e-Science • University of Queensland • University of Maryland • Others

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