1. Mike Hildreth representing the DASPOS project
DASPOS Update
Mike Hildreth
representing the DASPOS project

2. DASPOS Data And Software Preservation for Open Science
multi-disciplinary effort
Notre Dame, Chicago, UIUC, Washington, Nebraska, NYU, (Fermilab, BNL)
Links HEP effort (DPHEP+experiments) to Biology, Astrophysics, Digital Curation, and other disciplines
includes physicists, digital librarians, computer scientists
aim to achieve some commonality across disciplines in
meta-data descriptions of archived data
What’s in the data, how can it be used?
computational description (ontology/metadata development)
how was the data processed?
can computation replication be automated?
impact of access policies on preservation infrastructure

3. DASPOS
In parallel, will build test technical infrastructure to implement a knowledge preservation system
“Scouting party” to figure out where the most pressing problems lie, and some solutions
incorporate input from multi-disciplinary dialogue, use- case definitions, policy discussions
Will translate needs of analysts into a technical implementation of meta-data specification
Will develop means of specifying processing steps and the requirements of external infrastructure (databases, etc.)
Will implement “physics query” infrastructure across small- scale distributed network
End result: “template architecture” for data/software/knowledge preservation systems

4. DASPOS Overview Digital Librarian Expertise
How to catalogue and share data
How to curate and archive large digital collections
Computer Science Expertise
How to build databases and query infrastructure
How to develop distributed storage networks
Science Expertise
What does the data mean?
How was it processed?
How will it be re-used

5. DASPOS Process Multi-pronged approach for individual topics
NYU/Nebraska: RECAST and other developments
UIUC/Chicago: Workflows, Containers
ND: Metadata, Containers, Workflows, Environment specification
Shared validation & examples
Workshops & All-hands meetings
Shared collaboration with CERN, DPHEP
Outreach to other disciplines

6. Prototype Architecture
Container Cluster
Test bed
Capable of running containerized processes
“Containerizer Tools”
PTU, Parrot scripts
Used to capture processes
Deliverable: stored in DASPOS git
run
Preservation Archive
Metadata
Container images
Workflow images
Instructions to reproduce
Data?
store
Data Archive
Data
Tools:
Run containers/workflows
Discovery/exploration
Unpack/analyze
Policy & Curation
Access Policies
Public archives?
Domain-specific
Inspire
Data path
Metadata links

7. Prototype Architecture
Container Cluster
Test bed
Capable of running containerized processes
“Containerizer Tools”
PTU, Parrot scripts
Used to capture processes
Deliverable: stored in DASPOS git
run
Preservation Archive
Metadata
Container images
Workflow images
Instructions to reproduce
Data?
store
Data Archive
Data
Tools:
Run containers/workflows
Discovery/exploration
Unpack/analyze
Policy & Curation
Access Policies
Public archives?
Domain-specific
Inspire
~ Done
Under development
Not done
Data path
Metadata links

8. Infrastructure I: Environment Capture

9. Umbrella

10. Umbrella Current version of Umbrella can work with:
Docker – create container, mount volumes.
Parrot – Download tarballs, mount at run=me.
Amazon – allocate VM, copy and unpack tarballs.
Condor – Request compatible machine.
Open Science Framework – deploy uploaded containers
Example Umbrella Apps:
Povray ray-tracing application
OpenMalaria simulation
CMS high energy physics simulation

11. Infrastructure II: Workflow Capture

12. PRUNE

13. PRUNE Works across multiple workflow repositories
Is interfaced with Umbrella for environment specification on multiple platforms
reproducible, flexible workflow preservation

14. Infrastructure III: Metadata
HEP Data Model Workshop (“VoCamp15ND”)
Participants from HEP, Libraries, & Ontology Community*
*new collaborations for DASPOS
Define preliminary Data Models for CERN Analysis Portal
describe:
main high-level elements of an analysis
main research objects
main processing workflows and products
main outcomes of the research process
re-use components of developed formal ontologies
PROV, Computational Observation Pattern, HEP Taxonomy, etc.
Patterns implemented in JSON-LD format for use in CERN Analysis Portal
will enable discovery, cross-linking of analysis descriptions

15. Detector Final State Description
published paper at “International Conference on Knowledge Engineering and Knowledge Management”
Extraction ( of test data sets from CMS and ATLAS publications to examine pattern usability and ability facilitate data access across experiments

16. Computational Activity
Continued testing and validation of the Computational Activity and Computational Environment patterns
Work on aligning pattern with other vocabularies for software annotation and attribution, including Github and Mozilla Science led “Code as a research object” effort (

17. Overall Metadata work structure
Integration of patterns into a knowledge flow system that captures provenance and reproducibility information from a computational perspective as well as links to ”higher level” metadata descriptions of the data in terms of physics vocabularies

18. Technology I: Containers
Tools like chroot and Docker sandbox the execution of an application
Offer the ability to convert application to a container/image
Virtualize only essential functions of the compute node environment, allow local system to provide the rest
much faster computation
becoming the preferred solution over VMs for many computing environments
Native-execution time
49m2s
PTU Capture time
122m53s
PTU re-run time
114m05s
Native-execution in container[Docker]
58m40s
Server
Host OS
Docker Engine
Bin/Libs
App A
App B
Comparison of execution time for an ATLAS application using PTU (packaged environment, redirecting system calls) or a Docker container

19. Technology I: Containers
Portability = Preservation!
Tools like chroot and Docker sandbox the execution of an application
Offer the ability to convert application to a container/image
Virtualize only essential functions of the compute node environment, allow local system to provide the rest
much faster computation
becoming the preferred solution over VMs for many computing environments
Native-execution time
49m2s
PTU Capture time
122m53s
PTU re-run time
114m05s
Native-execution in container[Docker]
58m40s
Server
Host OS
Docker Engine
Bin/Libs
App A
App B
Comparison of execution time for an ATLAS application using PTU (packaged environment, redirecting system calls) or a Docker container

20. Technology II: Smart Containers

21. 4 5 Smart Containers Search Add machine-readable labels
API to write metadata
Metadata storage and strandardization
Specification of data location 4
Add machine-readable labels 5
Link things together into a knowledge graph

22. Containers Workshop

23. Containers Workshop

24. Containers Workshop
Captured surging interest in container technologies for all manner of applications
Attendees included:
Principle Software Engineer of RedHat
Senior Software Engineer for Docker
ReproZip developers
CS Specialists in containers and virtualization
Preservation examples:
OpenMalaria
Bertini (Numerical Algebraic Geometry)
HEP analysis

25. Technology III: CDF/DØ
As part of the effort to preserve software, executables, and data for the Tevatron experiments, we performed the pilot installation of the DØ code base outside of Fermilab.
use cvmfs to deliver code to any node outside of FNAL, executables running under VMs
integrated into batch system
data delivered remotely by SAM protocol
Used W mass as template analysis
Full analysis chain, including remote access to data, code, and executable versions of the software was demonstrated

26. CERN Analysis Preservation Portal
Tools:
Run containers/workflows
Preservation Archive
Metadata
Container images
Workflow images
Instructions to reproduce
Data?
Data Archive
Metadata
Data
Container Cluster
CERN OpenStack
“Containerizer Tools”
PTU, Parrot scripts
Used to capture processes
Deliverable: stored in DASPOS git
run
store

27. RECAST + CERN Analysis Portal
Streamlined demonstration of full preservation chain
“RECAST”: re-use of completed/preserved analysis with different inputs for data comparison
special schema developed specifically to describe these steps:
“packtivity” bundles executable (docker container), environment, executable description
specifies individual processing stages
“yadage”: captures how pieces fit together into a parametrized workflow
CERN Analysis Preservation Portal can store the descriptions of these processes
allows for re-use of stored processing chain

28. RECAST + CERN Analysis Portal
Workflow schematic:
As stored in CAP

29. RECAST + CERN Analysis Portal
Instructions and workflow descriptions can be extracted from CAP and used to instantiate jobs based on the stored info
prototype RECAST Cluster
infrastructure (website, result storage, message passing, job queue, workflow engine) fully dockerized themselves
Can deploy RECAST service instance to any docker swarm cloud (Carina, Google Container Engine, CERN Container Project)
each of these is a re-execution of a preserved ATLAS analysis

30. Collaborations/Spin-offs
RDA: Preservation/Reproducibility
Open Science Framework
pioneering Campus/OSF interactions
Wright State
Ontology specialists
National Data Service
Dashboard, archived computations, containers
DIANA
collaboration on goals, some preservation efforts

31. Next Steps Another scouting expedition?
Our goal is ultimately to change how science is done in a computing context so that it has greater integrity and productivity. We have developed some prototype techniques (in DASPOS1) that improve the expression and archival of artifacts. Going forward, we want to study how the systematic application of these techniques can enable new, higher level scientific reasoning about a very large body (multidisciplinary) of work.   For this to have impact, we will develop small communities of practice that will apply these techniques using the archives and tools relevant to their discipline. 
Another way to phrase this might be: to study/prototype the kinds of knowledge preservation tools that might make doing science easier and would enable broader/better science.