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Computational Storage Services (WP7 ForgetIT 1st year review)

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A consolidated platform for objects and computational processes (storlets) that will be defined, triggered and executed close to the data.

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Computational Storage Services (WP7 ForgetIT 1st year review)

  1. 1. Concise Preservation by combining Managed Forgetting and Contextualized Remembering
  2. 2. Simona Rabinovici-Cohen IBM Research - Haifa WP 7 Presentation Computational Storage Services ForgetIT 1st Review Meeting, April 29-30, 2014 Kaiserslautern, Germany
  3. 3. WP Objectives • Increase the value and outcome of preserved information over time –Provide additional incentive for preservation –Increase return-on-investment (ROI) • Transform the generic storage service to a richer service with potentially higher business value and automated preservation processes Focus of Year 1 • Build a consolidated platform for objects and computational processes (storlets) that will be defined, triggered and executed close to the data • Utilize the OpenStack Swift open source for cloud storage ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Objectives of WP and Year 1 Focus
  4. 4. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Role in Preserve-or-Forget Architecture
  5. 5. Leveraged PDS and Storlet Engine adding: Adapt Preservation Engine for ForgetIT Rules mechanism Storlets at interface proxy servers and local object servers Multiple programming languages for storlets New storlets: image transformation storlet fixity storlet concept detection storlet Searchable metadata contributions to OpenStack community Integration with whole ForgetIT framework Co-chair LTR group in SNIA to develop SIRF ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Achievements in Year 1
  6. 6. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Preservation DataStores (PDS) PDS offloads some archiving functionality to: Decrease probability of data loss Simplify the applications Provide improved performance and robustness Supports automation of archiving processes Provides computational storage via Storlet Engine PDS was also storage infrastructure of EU research projects CASPAR and ENSURE with partners: Europe Space Agency, Maccabi HMO, Tessella, Philips and more
  7. 7. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 PDS in OAIS Functional Model AIP • OAIS is ISO standard reference model for preservation (ISO:14721:2002) • Provide fundamental ideas, concepts and a reference model for long-term archives • Archival Information Package (AIP) - a logical structure for the preservation object that needs to be stored to enable future interpretation • Content Data Object (CDO) – raw data to be preserved PDS
  8. 8. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 DSpace and PDS
  9. 9. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 PDS Data Model Docket Costa Rica 2013 Docket Edinburgh Object (AIP) Aggregation Business Photos (silver) Object (AIP) Aggregation Private Photos (gold) Tenant Peter Stainer Hierarchical data model Tenant Aggregation and Tenant Docket object (AIP) Flexible organization of assets in collections with varied preservation policies (gold, silver, bronze) Aggregations support dynamic and transparent configuration of data management Metadata: aggregation=Private Metadata: aggregation=Business Docket Toy Conference 2014 Object (AIP) Object (AIP) Aggregation Press Releases (gold) Tenant Spielwarenmessen Metadata: aggregation=Press Metadata: aggregation=Press
  10. 10. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 The Need for Computational Object Storage • “Data is the new Oil” – In its raw form, oil has little value – Once processed and refined, it helps power the world • Data deluge of content depots and unstructured data – Documents, medical images, photos, videos, etc. – The fastest growing type of storage by volume – Object storage is ideal for this type of data • Object storage for content depots generally: – Utilizes large bandwidth to serve big data over the WAN – Uses server-based storage with under utilized CPUs • Process and refine the data where it is stored – Create a computational object storage with storlets “Data is the new oil.” Clive Humby
  11. 11. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Client Value for Using Storlets Reduce bandwidth – reduce the number of bytes transferred over the WAN e.g. Analytics storlet Enhance security – reduce exposure of sensitive data e.g. De-identification storlet Save costs – consolidate generic functions that can be used by many applications while saving infrastructure at the client side e.g. Curation storlet Support compliance – monitor and document the changes to the objects and improve provenance tracking e.g. Transformation storlet
  12. 12. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Storlet Engine Architecture
  13. 13. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Rules Mechanism Enables automatic conditional invocation of storlets Explicit storlet activation overrides implicit activation Rules kept as per tenant editable object, with specified access control Configured by tenant, user, role, container, object, content_type Wildcards (“*”) allowed in a rule (high flexibility) The first rule that matches the input is activated – prioritized list of rules Examples: De-Identification (per Role) Transformation (per Content Type) Fixity (per docket)
  14. 14. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Storlets at proxy node and object node L2 Rack Switch 1GB Ethernet account node - SSD L2 Rack Switch 1GB Ethernet L3 Switch 10GB Ethernet Virtual IP L3 Switch 10GB Ethernet container node -SSD object node - HDD object node - HDD proxy nodeproxy node Swift Object Node object service Swift Proxy Node Storlet Engine proxy service Storlet Engine
  15. 15. ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Fixity Storlet 16
  16. 16. • Papers • S. Rabinovici-Cohen, E. Henis, J. Marberg, K. Nagin, “Storlet Engine: Performing Computations in Cloud Storage”, to be submitted • S. Rabinovici-Cohen, R. Cummings, S. Fineberg, “Self-contained Information Retention Format For the, to be submitted • Posters • S. Rabinovici-Cohen (IBM), M. Baker (HP), R. Cummings (Antesignanus), S. Fineberg (HP), E. Henis (IBM), "Self-contained Information Retention Format (SIRF) in ForgetIT EU Project", 6th International Systems and Storage Conference (SYSTOR), 2013 • Other Dissemination Activities • The Storage Networking Industry Association (SNIA) published in its March 2013 Newsletter that SNIA Long Term Retention group formed a liaison with ForgetIT • The tutorial "Combining SNIA Cloud, Tape and Container Format Technologies for the Long Term Retention of Big Data" is given at several SNIA conferences • Deliverables • D7.1: Foundation of Computational Storage Services • D7.2: Computational Storage Services First Release ForgetIT Project GA600826, 1st Review Meeting, Kaiserslautern, April 2014 Publications
  17. 17. Thank you for your attention!

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