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Andrew Wiedlea - Wireless FasterData and Distributed Open Compute Opportunities and (some) Use Cases

Institute Director, Calit2 em California Institute for Telecommunications and Information Technology
30 de Mar de 2023
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Andrew Wiedlea - Wireless FasterData and Distributed Open Compute Opportunities and (some) Use Cases

  1. Wireless FasterData and Distributed Open Compute Opportunities and (some) Use Cases Andrew Wiedlea Science Engagement Team 9 February 2023
  2. ESnet is developing our strategy to support “the wireless edge” Motivation: ● Scientific progress will be completely unconstrained by the physical location of instruments, people, computational resources, or data. ● Collaborations at every scale, in every domain, will have the information and tools they need to achieve maximum benefit from scientific facilities, global networks, and emerging network capabilities. ● ESnet will foster partnerships and pioneer the technologies necessary to ensure that these transformations occur. A work in progress: a wide range of possibilities for what services and capabilities may be selected
  3. Science Use Case Drivers (short term) ● Convenience of IoT and the ability to deploy instruments in non-laboratory settings and hostile or constrained environments ● Within facilities, ease of relocation, equipment movement, and management ● Explosion of wireless options, each with different capabilities, cost models and uses makes data more fluid, less expensive to transport ○ 5G, Private Wireless/CBRS ○ LoRa ○ Zigbee ○ Starlink, Orbcomm, Non Terrestrial Networks ○ mmWave
  4. Longer term use case driver: Self Guided Field Laboratories and an automation revolution for scientific activity
  5. An enriched network + distributed compute + autonomous sensors = automated scientific mass “production” Abiven, Yves-Marie, Simon Bouvel, Thierry Bucaille, Leonard Chavas, Erik Elkaim, Patrick Gourhant, Youssef Liatimi, et al. 2020. “Robotizing SOLEIL Beamlines to Improve Experiments Automation.” In. JACoW Publishing, Geneva, Switzerland. https://doi.org/10.18429/JACOW-ICALEPCS2019-MOPHA001.
  6. BLUF: Enable the SGFL vision with PRP capabilities as part of Wireless FasterData? Can we deploy a ruggedized, affordable FIONA in the field for edge compute services and satisfy power, PRP mesh communications needs? Leverage self-driving 5G work underway already by PRP/NYU/ESnet Mariam Kiran, John Graham to test these concepts in the field? What opportunities are created for field science with a PRP distributed 5G core? We don’t know all the possible uses of wireless edge and network distributed compute, and we can’t know until we deploy something at reasonable scale…. And the reason is that our work both supports and drives science possibilities.
  7. SAIL and SGFL
  8. SGFL Example: Water Cycle APPROACH: ●Generating actionable information based on model execution and data fusion ○ CSA: implementing model on HPC with autonomous data ingestion ○ EESA: Executing the model to generate predictions informed by lab (Bioepic) and field (East River) measurements ○ CSA and EESA: integration of model with other large-scale datasets to identify uncertainty distribution to guide adaptation/feedback ●Integrated lab/field data streams to guide further sampling in the field o CSA: Data workflow and harmonization o EESA: ET model sensitivity analysis targeting a range of field scenarios
  9. SGFL Example: NRT Earthquake Impact Analysis Exploring a combination of NTN and cellular to ensure rapid movement of structural response data after a quake to inform large area damage simulation based estimation Petrone Floriana, Perez Rowland, Coates Jason, and McCallen David. 2023. “A Biaxial Discrete Diode Position Sensor for Rapid Postevent Structural Damage Assessment.” Journal of Structural Engineering 149 (3): 04022251.
  10. SGFL Example: Urban & Radiation Mapping Urban radiation effects are a complex combination of: ● Soil and building materials ● Weather & atmosphere ● Human behaviors Detection of anomalies is inherently a short range, multi-mode/multi-int game, requiring both distributed analytics and interesting patterns of pushing data to the field / multi-level simulation (1D-3D)
  11. SGFL Example: Energy Grid Work is underway to deploy a integrated 10K device/simulation/human in the loop multi-lab platform for future energy grid technology study. Requires a low jitter/predictable latency dedicated network, using ESnet. Integrated predictable wireless (such as via distributed core) and edge compute to provide low delay data tagging and reduction across standards may well develop as a requirement.
  12. SGFL Example: Offshore Wind Power ECP’s ExaWind project/LBL/NREL research underway on development of algorithms supporting wind turbine design and placement optimization. Very large, offshore tower concepts are being developed. Larger is more efficient, but also will create greater demands for more remote placement, increase data movement/compute demands Testbeds will absolutely provide opportunities for wireless edge compute, present interesting opportunities for research and control communications (weather, tunable blades, fault and grid analysis)
  13. Desired End State Thoughts 1) SGFL capabilities will be driven as much by what we as “an enriched network” community dream up, as by what our current customers request 2) The integration of a federated wireless core with the enriched network could jump start national progress towards SGFL 3) Now is a good time to take some risks; the technology space is fluid and we can’t count on being right, but we also know that we can’t afford to fall behind and let the market or near-peer competitors define our options 4) Let’s get nodes out into the field, and on orbit. I believe there could be an interesting opportunity to work with one or more commercial providers*

Notas do Editor

  1. Digital twin plus fluid data flow plus an enriched network (network+data+storage)
  2. Scotty Strachan shout out.
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