network time protocol from a distributed timescale traceable ... - Netnod
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network time protocol from a distributed timescale traceable ... - Netnod
â¢Store all data in a transactional DHT (Distributed Hash. Table) database, where the I/O nodes are the data nodes. Thi
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NETWORK TIME PROTOCOL FROM A DISTRIBUTED TIMESCALE TRACEABLE TO UTC Netnod spring meeting 2016 Ragnar Sundblad
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Agenda •Background & overview •Services delivered •Node overview, node structure •Hardware (FPGA) NTP server •Time transfer •Network •Future work •Conclusion •Questions
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Background & overview •Funded by the Swedish Post and Telecom Authority - PTS
•Distributed timescale in 5 locations
•Placed at Netnod IX •Each node autonomous •Traceable to UTC(SP)
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Services delivered Live data available: • NTP, unicast and anycast, IPv6 and IPv4, authenticated • Comparisons to UTC(SP) and other labs • Time transfer data (GNSS) • Local clock measurements • Steering data, operational data
Node overview Two node halves Distribution (1PPS, 5MHz & 10MHz) Measurement and control NTP IPv4 & IPv6 PTP/IEEE 1588v2 and SyncE, E1/2048 kHz GNSS time transfer Backbone fiber network Data I/O Backup power
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Node structure
Sat Rcvr Data services
Steer
Cs Clock
Freq & Phase offset shift
NTP 4 x 10 Gb
1588 / PTP & 2048 kHz Steer
Cs Clock
Freq & Phase offset shift
NTP 4 x 10 Gb
Data services PTP Time Xfr
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Hardware (FPGA) NTP server • Fast (4*10Gb/s) • IPv4 & IPv6 • Secure • Symmetric key authentication
• Interface card for input and output (1PPS & 10MHz)
• Open source FPGA code
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Time Transfer - multiple methods used
If methods disagree: Do nothing and sound alarm
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Network Dedicated wavelengths Backup to GNSS 1588v2 (PTP)
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Future work •100 Gb or possibly analog, optically coherent, long haul time, frequency and phase transfer.
•Store all data in a transactional DHT (Distributed Hash Table) database, where the I/O nodes are the data nodes. This would allow near real time access to all data and results.
•Provide better data integrity and authentication/signing of measurement data tied to actual equipment calibrations.
•More software with machine learning to automatically resolve operational problems.
