HEP and ESnet: Synergies, Collaboration, Opportunities

1. HEP and ESnet: Synergies, Collaboration, Opportunities Gregory Bell, Ph.D. Director, Energy Sciences Network (ESnet) Director, Scientific Networking Division Lawrence Berkeley National Laboratory DOE / Germantown Washington, DC July 31, 2015

2. ESnet & OHEP: collaborating for almost 30 years.

3. ESnet is a special-purpose mission network, funded by Congress to support the scientific goals of the Department of Energy. We see networking as a means to an end: scientific productivity. We aim to create a world in which discovery is unconstrained by geography.

4. The basic facts, with a HEP inflection: • High-speed international networking facility, optimized for data-intensive science: • connecting 50 labs, plants and facilities with >150 networks, universities, research partners globally • supporting every HEP frontier and experiment, and serving as an integral extension of many physics instruments • 340Gbps transatlantic extension in production since Dec 2014 • >1.3 Tbps of external connectivity, including high speed access to commercial partners such as Amazon • growing number university connections to better serve LHC science • older than commercial Internet, growing ~twice as fast • $62M ARRA grant funded 100G upgrade in 2011: • fiber assets + access to spectrum, shared with Internet2 • new era of optical networking, abundant capacity • world’s first 100G network at continental scale (now deploying first 400G production link)

5. This is not an ISP. It’s a DOE user facility designed to overcome the constraints of geography. We do this by offering unique capabilities, and optimizing the facility for data acquisition, data placement, data sharing, data mobility.

6. 80% of ESnet traffic originates or terminates outside the DOE complex. ESnet’s success is necessary but not sufficient for DOE, because research networks all share fate. SC invests $1B/year in university research: campus networks matter.

7. June 2015: 23.2 PB

8. June 2015: 23.2 PB

9. Emerging role for ESnet: ‘superfacility’ glue. Experimental facilities are being transformed by new detectors, advanced mathematics, robotics, automation, programmable networks. 1) Detectors soon capable of generating terabit data streams. 2) Computational tools for analysis, data reduction & feature extraction in situ, using advanced algorithms and special-purpose hardware. 6) Integration of experimental and computational facilities in real time, using programmable networks. 5) Data management and sharing, with federated identity management and flexible access control. 3) Increase scientific throughput from robotics and automation software. 4) Post-processing: reconstruction, inter-comparison, simulation, visualization.

10. Data flow from single LCLS detector triplesnetwork utilization for major HPC center.

12. ESnet >> Network: Tools and APIs Highly usable, web-based tools for scientists, engineers, CIOs, DOE PMs • shedding light inside ‘black box’ of the network • providing real-time data to researchers • http://my.es.net

13. ESnet >> Network: Science Engagement Improving science outcomes through: • direct engagement with data-intensive communities • current projects: climate, BES beamlines • requirements gathering • reports for each SC Office, every three years • network requirements derived from ‘process of science’ case studies • development and global advocacy of best practices • information, tools, knowledgebase • http://fasterdata.es.net/

14. ESnet >> Network: R&D, Innovation Promise of programmability: real-time integration of facilities, network circuits for deterministic performance, virtual topologies, security enclaves, network ‘apps’…

15. ESnet SDN Testbed (in deployment) ESnet PE Router (2+)x10GE SDN Testbed node locations SDN Testbed connectivity overlay (using OSCARS circuits) (n)x10GE Testbed Host STAR AMST DENV AOFA LBL WASH CERN ATLA

16. Carefully-coordinated response to recent ASCR FOA on software defined networking: • SENSE: SDN for End-to-end Networked Science at the Exascale • ESnet proposal for building a multi-domain, multi-layer, federated model for end-to-end SDN between next-generation Science DMZs • System Monitoring, Modeling and Optimization with Machine Learning • Caltech proposal as Appendix 7 of SENSE • Predictive analysis using leveraging simulation, modeling, monitoring and machine learning techniques. • End-systems, site networks, and wide area networks • SDN-NGenIA • Caltech’s single PI proposal, focus on Intelligent services with application, Site perspective • Smart middleware to use network services intelligently, including knowledge of compute/storage, monitoring, predictive analysis • Optimized for the LHC use-case

17. Discovery, unconstrained by geography.

18. Thank you. http://www.es.net/ http://fasterdata.es.net/ http://my.es.net/ greg@es.net

19. Isolated apps are less flexible than platforms. Single-purpose apps: Programmable platform:

20. An architecture we foresee: ESnet as platform for concurrent, domain-specific network apps. Requires ‘network operating system’ for science. • early-stage project, LDRD proposal • multiple challenges in creating flexible, stable app execution environment We envision apps that will: • create and manage virtual networks • enable programmatic resource allocation • optimize link utilization Future apps could support: • NDN for climate; data management for CMS, ATLAS, Belle-II; security overlay for KBase; replication for ESGF; detector / HPC coupling for light sources • science workflows we haven’t imagined Network platform architecture presents obvious collaboration opportunities.