1. Evolving Data Logistics Needs of the LHC Experiments Paul Sheldon Vanderbilt University

2. Outline To meet its computing and storage needs (and socio/political and funding needs), the LHC experiments deployed a globally distributed “grid” computing model. Successfully demonstrated grid concepts in production at an extremely large scale – enabled significant science! Short term (2 years) and longer term (10 years) projects will require them to significantly evolve their computing model. Wide area distributed storage Agile, intellegent network based solutions: DYNES and ANSE Thanks to Artur Barczyk, Ken Bloom, Eric Boyd, Oli Gutsche, Ian Fisk, Harvey Newman, Jim Shank, Jason Zurawski, and others for slides and ideas. September 5, 2013Emerging Data Logistics Needs of the LHC Expts2

3. A High Profile Success September 5, 2013Emerging Data Logistics Needs of the LHC Expts3

4. A High Profile Success September 5, 2013Emerging Data Logistics Needs of the LHC Expts4 The LHC would not have been successful without grid computing, data logistics, and robust high performance networks

5. Distributed Compute Resources and People September 5, 2013Emerging Data Logistics Needs of the LHC Expts5 CMS Collaboration: 38 Countries, >163 Institutes

6. Distributed Compute Resources and People was not possible or even desirable Sociology, Politics, Funding LHC Experiments decided early on that it was not possible or even desirable to put all or even most computing resources at CERN: Sociology, Politics, Funding Physics is done in small groups, which are themselves geographically distributed To maximize the quality and rate of scientific discovery, all physicists must have equal ability to access and analyze the experiment's data To maximize the quality and rate of scientific discovery, all physicists must have equal ability to access and analyze the experiment's data September 5, 2013Emerging Data Logistics Needs of the LHC Expts6

7. Distributed Compute Resources and People was not possible or even desirable Sociology, Politics, Funding LHC Experiments decided early on that it was not possible or even desirable to put all or even most computing resources at CERN: Sociology, Politics, Funding Physics is done in small groups, which are themselves geographically distributed To maximize the quality and rate of scientific discovery, all physicists must have equal ability to access and analyze the experiment's data To maximize the quality and rate of scientific discovery, all physicists must have equal ability to access and analyze the experiment's data September 5, 2013Emerging Data Logistics Needs of the LHC Expts7 Adopt a grid-like computing model, use those pieces of grid concepts and tools that were “ready” and needed.

8. Tiered Computing Model September 5, 2013Emerging Data Logistics Needs of the LHC Expts8 Tier 0 LHC raw data logged to the “Tier 0 ” compute center at CERN. T 0 does initial processing, verification and data quality tests.

9. Tiered Computing Model September 5, 2013Emerging Data Logistics Needs of the LHC Expts9 Tier 1 Tier 1’s Tier 0 Data is then distributed to Tier 1 centers, the top level centers for each region of the world (US CMS Tier 1 is at Fermilab). These process the data to create “data products” that physicists will use Not all Tier 1’s are shown 20 Gbs dedicated network

10. Tier 2 and Tier 3 Centers 8 Regional Tier 2 ’s in the US, 53 worldwide. Tier 3 ’s serve individual Universities Tier 2 and 3 ’s provide the computing and storage used by physicists to analyze the data & produce physics. Physicists work in small groups, but those groups may be globally distributed. September 5, 2013Emerging Data Logistics Needs of the LHC Expts10 Data Flow

11. Analysis Projects are Mostly Self-Organized Worker nodes at each T2/3 site present a batch system Access through grid interface/certs (no local access) User analysis workflows built and submitted by “grid enabled” software framework (CRAB, PanDA) breaks tasks into jobs – decides where to run them – monitors them – retrieves the output when they are done. Data tethering: originally jobs could only run at a site if they were hosting the required data locally Evolving model (more later) is relaxing this requirement September 5, 2013Emerging Data Logistics Needs of the LHC Expts11

12. How is it Working? 200K – 300K LHC jobs launched/day September 5, 2013Emerging Data Logistics Needs of the LHC Expts12 CMS Tier 1 CMS Tier 2 CPU Hours (HS06h) 1 HS06h ~ 1 GFLOP hour http://w3.hepix.org/benchmarks/doku.php/

13. Data Movement Some data is strategically placed based on high level “management” decisions Users can also request datasets be placed at “friendly sites” where resources have been made available to their analysis group. (They can whitelist those sites in CRAB/PanDA.) System for Organized Transfers (PhEDEx in CMS)… Destination based transfer requests (transfer dataset X to site A) System picks out source sites based on link quality and load (several source sites might be used) PhEDEx schedules GridFTP transfers between sites through several layers of software Software layers protect sites, enable multiple streams and take care of authentication (Globus tools, SRM, FTS, etc.) September 5, 2013Emerging Data Logistics Needs of the LHC Expts13

14. How is it Working? September 5, 2013Emerging Data Logistics Needs of the LHC Expts14 ATLAS CMS 2012 Throughput (MB/S) 2012 AVG: ~5 GB/s AVG: ~1 GB/s Combined: 180 PB/year Aggregate Bandwidth on all Tier N Links

15. Model Has Evolved Originally strictly hierarchical In addition to strategic pre- placement of data sets… Meshed data flows: Any site can use any other site to get data Dynamic data caching: Analysis sites will pull datasets from other sites “on demand” Remote data access: Jobs executing locally access data at remote sites in quasi-real time September 5, 2013Emerging Data Logistics Needs of the LHC Expts15

16. Near Term Data Requirments: LHC Run 2 LHC Run 2 (begins 2015): Data xfer volume 2-4 times larger Energy increase 8 to 13 TeV – more complex events, larger event sizes Trigger rate increase from 300-400 Hz to 1 kHz Dynamic data placement and cache release replicate samples in high demand automatically Release cache of replicas automatically when demand decreases Establish CMS data federation for all CMS files on disk Allow remote access of files with local caching as job runs processing can start before samples arrive at destination site Higher demand for sustained network bandwidth, fill gaps btwn bursts Increase diversity of resources: get access to more resources September 5, 2013Emerging Data Logistics Needs of the LHC Expts16

17. 10 Year Projections Requirements in 10 years? Look 10 years back… September 5, 2013Emerging Data Logistics Needs of the LHC Expts17 Source: Ian Fisk and Jim Shank

18. Fisk and Shank 10 Year Projections The processing has increased by a factor of 30 in capacity This is essentially what would be expected from a Moore’s law increase with a 2 year cycle Says we spent similar amounts Storage and networking have both increased by factor of 100 10 times trigger and 10 times event size In their judgment, future programs will likely show similar growth “How to make better use of resources as the technology changes?” September 5, 2013Emerging Data Logistics Needs of the LHC Expts18 Fisk and Shank, Snowmass Planning Exercise for High Energy Physics

19. Fisk/Shank View of the Future September 5, 2013Emerging Data Logistics Needs of the LHC Expts19

20. …Fisk/Shank View of Future September 5, 2013Emerging Data Logistics Needs of the LHC Expts20

21. …Fisk/Shank View of Future September 5, 2013Emerging Data Logistics Needs of the LHC Expts21

22. DYNES: Addressing Growing/Evolving Network Needs NSF MRI-R2: DYnamic NEtwork System (DYNES) A nationwide cyber-instrument spanning ~40 US universities and ~14 Internet2 connectors Extends Internet2/ESnet Dynamic Circuit tools (OSCARS, …) Who is it? Project Team: Internet2, Caltech, Univ. of Michigan, and Vanderbilt Univ. Community of regional networks and campuses LHC, astrophysics community, OSG, WLCG, other virtual organizations What are the goals? Support large, long-distance scientific data flows in the LHC, other data intensive science (LIGO, Virtual Observatory, LSST,…), and the broader scientific community Build a distributed virtual instrument at sites of interest to the LHC but available to R&E community generally September 5, 2013Emerging Data Logistics Needs of the LHC Expts22

23. Why DYNES? LHC exemplifies growing demand for research bandwidth LHC “Tiered” computing infrastructure already encompasses 140+ sites moving data at an average rate of ~5 GB/s LHC data volumes and transfer rates are expected to expand by an order of magnitude over the next few years US LHCNet, for example, is expected to reach 280-400 Gbps by 2014 between its points of presence in NYC, Chicago, CERN and Amsterdam Network usage on this scale can only be accommodated with planning, an appropriate architecture, and nationwide community involvement By the LHC groups at universities and labs By campuses, regional and state networks connecting to Internet2 By ESnet, US LHCNet, NSF/IRNC, major networks in US & Europe September 5, 2013Emerging Data Logistics Needs of the LHC Expts23

24. DYNES Mission/Goals Provide access to Internet2’s dynamic circuit network in a manageable way, with fair-sharing Based on a ‘hybrid’ network architecture utilizing both routed and circuit based paths. Enable such circuits w/ bandwidth guarantees across multiple US network domains and to Europe Will require scheduling services at some stage Build a community with high throughput capability using standardized, common methods September 5, 2013Emerging Data Logistics Needs of the LHC Expts24

25. Why Dynamic Circuits? Internet2’s and ESnet’s dynamic circuits services provide: Increased effective bandwidth capacity, and reliability of network access, by isolating traffic from standard IP “many small flows” traffic Guaranteed bandwidth as a service by building a system to automatically schedule and implement virtual circuits Improved ability of scientists to access network measurement data for network segments end-to-end via perfSONAR monitoring Static “nailed-up” circuits will not scale. GP network firewalls incompatible with enabling large-scale science network dataflows Implementing many high capacity ports on traditional routers would be expensive September 5, 2013Emerging Data Logistics Needs of the LHC Expts25

26. DYNES Dataflow September 5, 2013Emerging Data Logistics Needs of the LHC Expts26 user requests via a DYNESTransfer URL DYNES provided equipmentat end sites and regionals(Inter-Domain Controllers,Switches, FDT Servers) DYNES Agent will providethe functionality to request a circuit instantiation, initiate and manage the data transfer, and terminate the dynamically provisionedresources. Site A Site Z Dynamic Circuit

27. DYNES Topology September 5, 2013Emerging Data Logistics Needs of the LHC Expts27 Based on Applications Rcvd from Potential Sites

28. ANSE: Integrating DYNES into LHC Computing Frameworks NSF CC-NIE: Advanced Network Services for Expts (ANSE) Project Team: Caltech, Michigan, Texas-Arlington, & Vanderbilt Goal: Enable strategic workflow planning including network capacity as well as CPU and storage as a co-scheduled resource Path Forward: Integrate advanced network-aware tools with the mainstream production workflows of ATLAS and CMS Use in-depth monitoring and network provisioning Complex workflows: a natural match and a challenge for SDN Exploit state of the art progress in high throughput long distance data transport, network monitoring and control September 5, 2013Emerging Data Logistics Needs of the LHC Expts28

29. ANSE Methodology Use agile, managed bandwidth for tasks with levels of priority along with CPU and disk storage allocation. define goals for time-to-completion, with reasonable chance of success define metrics of success, such as the rate of work completion, with reasonable resource usage efficiency define and achieve “consistent” workflow Dynamic circuits a natural match Process-Oriented Approach Measure resource usage and job/task progress in real-time If resource use or rate of progress is not as requested/planned, diagnose, analyze and decide if and when task re-planning is needed Classes of work: defined by resources required, estimated time to complete, priority, etc. September 5, 2013Emerging Data Logistics Needs of the LHC Expts29

30. Summary and Conclusions Successful globally distributed computing model has been extremely important for LHC’s success Have intentionally not been bleeding-edge, but have certainly validated the distributed/grid model For the future, CPU may scale sufficiently but we can’t afford to grow storage by a factor of 100 Agile, large, intelligent network-based systems are key Leads to less bursty, more sustained use of networks Projects such as DYNES and ANSE are essential first steps as the LHC evolves to what Harvey Newman calls a data intensive Content Delivery Network September 5, 2013Emerging Data Logistics Needs of the LHC Expts30