1. Information Management and Distributed Data Reagan W. Moore Wayne Schroeder Mike Wan Arcot Rajasekar Richard Marciano {moore, schroede, mwan, sekar, marciano}@sdsc.edu {moore, schroede, mwan, sekar, marciano}@sdsc.edu http://www.sdsc.edu/srb http://irods.sdsc.edu/

2. Data Grid Software Storage Resource Broker http://www.sdsc.edu/srb iRODS - integrated Rule-Oriented Data System http://irods.sdsc.edu Version 0.9 released May 30, 2007 Version 1.0 scheduled for fall, 2007 Open source - BSD license

3. Concepts Distributed Data Management Concepts Data virtualization Infrastructure independence Trust virtualization Administrative domain independence Federation Rule-based Data Management Management virtualization Automating execution of management policies Coupling management policies to assertions about data

4. Data Virtualization Manage properties of each digital entity independently of the remote storage systems Infrastructure independence Properties Name spaces Persistent state information (location, size,…) Manage standard operations Client actions Operations performed at remote storage systems

5. Data Virtualization Storage System Storage Protocol Access Interface Standard Access Actions Data Grid Map from the actions requested by the access method to a standard set of micro-services used to interact with the storage system Standard Micro-services

6. Federation 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

7. Production Data Grids: Observations Data grids manage shared collections that are distributed across multiple storage systems and institutions Data grids are responsible for providing recovery mechanisms for all errors that occur in the distributed environment The number of observed problems is proportional to the size of the collections Need to minimize labor costs by automating: Application of management policies Execution of administrative functions for error recovery Validation of preservation assessment criteria

8. Observations of Production Data Grids Each community implements different management polices Need a mechanism to support the socialization of shared collections Community specific preservation objectives Community specific assertions about properties of the shared collection Community specific management policies

9. iRODS What additional levels of virtualization are required to support advanced data management applications? Observe that each community imposes different management policies. Different criteria for data retention, disposition, access control, data caching, replication Assertions on collection integrity and authenticity such as required metadata Assertions on data distribution, data transport Need the ability to characterize management policies, automate their application, and verify collection properties

10. Socialization of Data Collections Management policies are a mechanism for the "socialization" of a collection. The management policies describe how the collection can be accessed by a broader community, the internal consistency mechanisms that maintain the reputation of the builders of the collection, and the collection consistency properties that the broader community can expect when they access the data. Management policies transform from the expectations of the designated community that built the collection to the expectations of the wider world that uses the collection. While management policies are unique for each record collection, generic management policies exist that can be tuned to represent the "socialization" of the collection.

11. Data Management iRODS - integrated Rule-Oriented Data System

12. Rule-based Data Management Map from management policies to rules controlling execution of remote micro- services Manage persistent state information for results of each micro-service execution Support an additional three logical name spaces Rules Micro-services Persistent state information

13. iRODS - integrated Rule- Oriented Data System Resources Client InterfaceAdmin Interface Metadata Modifier Module Config Modifier Module Rule Modifier Module Consistency Check Module Confs Rule Base Metadata Persistent Repository Engine Rule Current State Rule Invoker Micro Service Modules Resource-based Services Micro Service Modules Metadata-based Services Service Manager Consistency Check Module Consistency Check Module

14. Management Virtualization Standard policies expressed as rules Rules control execution of data management and access operations Integrity Validation of checksums Synchronization of replicas Data distribution Data retention Access controls Authenticity Chain of custody - audit trails Required preservation metadata - templates Generation of AIPs, DIPS

15. Example Rules Rule composed of four parts: Name | condition | micro-service set | recovery Rule to automate replication of data for a specific collection acPostProcForPut | $objPath like /tempZone/home/rods/nvo/* | msiSysReplDataObj(nvoReplResc,null) | nop Rule types Internal, administrative, user-defined Atomic, deferred, periodic

16. Three Classes of Rules Internal rules Used within iRODS for standard data manipulation services Administrator rules Set by data grid administrator to enforce policies on shared collection User-defined rules Support server-driven workflows

17. Rule-based Data Management Associate rules with combinations of name spaces Rule set for a particular collection Rule set for a particular user group Rule set for a particular user group when accessing a particular collection Rule set for a particular storage system Rule set for a particular micro-service Generic rules based on SRB operations

18. Administrative Rules Currently 15 administrative rules Administrative Storage selection Data pre-processing Data post-processing Data deletion Parallel I/O

19. Creation Rules Administration Creation Rules acCreateUser | | msiCreateUser## acCreateDefaultCollections## msiCommit | msiRollback## msiRollback##nop acVacuum(*arg1) | | delayExec(msiVacuum,*arg1) | nop acCreateDefaultCollections | | acCreateUserZoneCollections | nop acCreateUserZoneCollections | | acCreateCollByAdmin(/$rodsZoneProxy/home,$otherUserName)## acCreateCollByAdmin(/$rodsZoneProxy/trash/home,$otherUserName) | nop##nop acCreateCollByAdmin(*parColl,*childColl) | | msiCreateCollByAdmin(*parColl,*childColl) | nop

20. Administration Deletion Rules acDeleteUser | | acDeleteDefaultCollections## msiDeleteUser## msiCommit | msiRollback##msiRollback##nop acDeleteDefaultCollections | | acDeleteUserZoneCollections | nop acDeleteUserZoneCollections | | acDeleteCollByAdmin(/$rodsZoneProxy/home,$otherUserName)## acDeleteCollByAdmin(/$rodsZoneProxy/trash/home,$otherUserName) | nop##nop acDeleteCollByAdmin(*parColl,*childColl) | | msiDeleteCollByAdmin(*parColl,*childColl) | nop

21. Data Manipulation Rules Rule for pre-processing on storage use acSetRescSchemeForCreate | | msiSetDefaultResc(demoResc,noForce)## msiSetRescSortScheme(random)## msiSetRescSortScheme(byRescType) | nop##nop##nop Rule for pre-processing on data reads acPreprocForDataObjOpen | | msiSortDataObj(random) | nop Rule for post processing data writes acPostProcForPut | | nop | nop acPostProcForCopy | | nop | nop Rule for setting number of threads for parallel I/O acSetNumThreads | | msiSetNumThreads(default,default,default) | nop Rule for data deletion policy setting acDataDeletePolicy | | nop | nop

22. Planned Development Implement the rules and micro-services needed for the listed ERA capabilities Have identified 174 micro-services Data manipulation Structured information manipulation Have identified 212 persistent state attributes Implement the rules and micro-services needed to validate assessment criteria for trusted digital repositories Have identified 176 rules

23. ERA Capability Categories Accession Arrangement Description Preservation Access Disposition Subscription Notification Task queuing Transformative migration Display transformation Automated client specification System performance and failure reports.

24. Summary of Mapping ERA Capabilities to Management Rules ERA integrates capabilities of multiple systems PAWN submission pipeline - 34 operations Cheshire indexing system - 13 operations Kepler workflow - 53 operations iRODS data management - 597 operations Operations facility - the remaining capabilities The 597 operations are executed by 174 generic rules The analysis identified five types of metadata attributes: Collection metadata - 11 attributes File metadata - 123 attributes User metadata - 38 attributes Resource metadata - 9 attributes Rule metadata - 32 attributes

25. Example ERA Capabilities Record manipulation List files Display file (template) Format file Delete file Delete file authorized Delete file copies Delete file versions Erase file Replace file Set file version Create soft link Replicate file Synchronize replicas Physically move file Annotate file Access URL Check vault Monitor space used Output file Register file Regenerate system metadata Set number of items per display page Structured information DIP format template Disposition agreement format template Disposition action format template Physical location report template Inventory report template Data movement summary report template Access report template File migration report template Document internal access control template AIP format template Transfer format template Access review determination rule template Access review determination report template Validate access classification rule template File transfer discrepancy report template Notification review report template Redaction rule template Search display template File display template (file type) Format conversion format template Workbench display template Request help format template

26. Theory of Data Managment  Characterization  Persistent name spaces  Operations that are performed upon the persistent name spaces  Changes to the persistent state information associated with each persistent name space that occur for each operation  Transformations that are made to the records on each operation  Completeness  Set of operations is complete, enabling the decomposition of every data management process onto the operation set.  Data management policies are complete, enabling the validation of all data assessment criteria.  Persistent state information is complete, enabling the validation of authenticity and integrity and management policies.  Assertion  If the operations are reversible, then a future data management environment can recreate a record in its original form, maintain authenticity and integrity, support access, and display the record.  Such a system would allow records to be migrated between independent implementations of data management environments, while maintaining authenticity and integrity.