Networking in the ‘Jetson Age’

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Inder Monga, Director, Energy Sciences NetworkDivision Director, Scientific Networking, LBNL

ESnet: Department of Energy’s high-performance science network facility

ESnet provides connectivity toall of the DOE labs, experiment sites, &

supercomputers

Our vision:

Scientific progress will be completely unconstrained by the physical location of instruments, people, computational resources, or data.

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Our vision:

Scientific progress will be completely unconstrained by the physical location of instruments, people, computational resources, or data.

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Data over distance

Global partnerships with other science networks (NRENS) around the world is key

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When will I get home?

LAX– Caltech, 6 pm:1 hr – 1hr 50 min

LAX– Caltech, 11 pm: 32 min

• Transfers over a shared network are not predictable• Best-effort delivery can also mean worst-effort delivery

Challenge: Predictable Network Transfers at scale

Chard K, Dart E, Foster I, Shifflett D, Tuecke S, Williams J. (2018) The

Modern Research Data Portal: a design pattern for networked, data-

intensive science. PeerJ Computer

Science4:e144 https://doi.org/10.7717/peerj-cs.144

Nine orders of

variability for Petrel

Application Workflow

Agent

Failure on a network element, problem fixed

15 Gbps P2P service between Caltech and Fermilab Instantiated

20 Gbps available for a P2P service between Caltech and Fermilab

Endpoint Listing

Challenge: Applications cannot ‘dialogue’ with the network

Please provide a listing of all available provisioning

Endpoints

What is the maximum bandwidth available for a

P2P service between Caltech and Fermilab?

Request 20 Gbps P2P service between Caltech and

Fermilab. If 20 Gbps not available 10Gbps is ok.

Service is not working, please check status

Please provide a listing of all available provisioning

Endpoints

Endpoint Listing

What is the maximum bandwidth available for a

P2P service between Caltech and Fermilab?

20 Gbps available for a P2P service between Caltech and Fermilab

Request 20 Gbps P2P service between Caltech and

Fermilab. If 20 Gbps not available 10Gbps is ok.

15 Gbps P2P service between Caltech and Fermilab Instantiated

Service is not working, please check status

Failure on a network element, problem fixed

Machine Learning applied to network telemetry data –learn, understand and optimize

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Training input

Training output

Sliding Window

Predicting traffic per link/siteMethod: Deep Machine Learning (Recurrent Neural Network) for time-series data

• Predict anomalies (or peaks) accurately 15 minutes in future

• Graph showing 1 minutes prediction in future

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Language processing to take intent input

• Automate rendering into network commands like bandwidth, time schedule, topology

• Optimize the network• Return success or failure to user

• Understand English (e.g. transfer, connect)• Check conditions, conflicts and permissions• ML in Natural Language Processing for

intelligent negotiation with user

NLP, OWL, “AI” Network configuration

Renderer translates intent

intent

Network state

iNDIRA

“ I want to send data to my SuperComputer at NERSC by 5:00pm today”

“ Ok ill reconfigure the network to make this possible!”

Machine Learning applied to dialogue with applications –understand the intent

Thank you!

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Vision (or a challenge): A cognitive network

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Network can:

Assimilates internal and external information (e.g. usage, maintenance schedules, component MTF, driver’s schedule, etc.)

Anticipates trips based on routines and disruptions (e.g. scheduled maintenance)

Adapts route and departure time due to road, (current and expected) traffic, and weather conditions

Machine Learning applied to network telemetry data –learn, understand and optimize

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Anomalies in link performance Method used:

ARIMA

Classifying flows across DOE sites Method: Gaussian

Mixture Models

Predicting traffic topologies across DOE sites Method: Markov Models

ESnet is built to handle science’s ‘big’ data whose traffic patterns differ dramatically from the Internet

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Science ‘big’ data

VideoCloud Apps

Internet

Applications care about throughput, not bandwidth

Measured (TCP Reno) Measured (HTCP) Theoretical (TCP Reno) Measured (no loss)

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. See Eli Dart, Lauren Rotman, Brian Tierney, Mary Hester, and Jason Zurawski. The Science DMZ: A Network Design Pattern for Data-

Intensive Science. In Proceedings of the IEEE/ACM Annual SuperComputing Conference (SC13), Denver CO, 2013.

Metro Area

Local(LAN)

Regional

Continental

International

20x performance gap due to 1 packet lost in 22000 packets (0.0046%)