Mesh Network Clock Syntonization Using Loop Drift Correction
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Solution Overview
Problem
Current clock synchronization methods in networked systems, such as data centers and distributed ledgers, face limitations in achieving high accuracy due to frequency drift caused by environmental factors, leading to inefficiencies and fairness issues, particularly in finance and e-commerce applications, where millisecond-level synchronization is commonly accepted despite the potential for nanosecond-level precision.
Innovation Solution
The implementation of a system that uses a coordinator to continuously adjust clock offset and frequency drift using network observations and adaptive stochastic control, allowing for precise synchronization of clocks to within nanosecond accuracy without requiring specialized hardware, by employing advanced filtering techniques and machine learning models to resist noise and variability in clock responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If millisecond-level clock synchronization algorithms (e.g., NTP) are used, then device complexity and cost are reduced, but clock synchronization accuracy deteriorates to millisecond level
Solution Approach 1:
The patent changes the time measurement parameter from millisecond level to nanosecond level by using high-resolution timestamps and measuring one-way network delays with nanosecond precision. This allows commodity clocks to achieve nanosecond-level synchronization accuracy without requiring specialized hardware, resolving the contradiction between device complexity and measurement precision.
2Measurement precision
If nanosecond-level clock synchronization is implemented, then clock synchronization accuracy is improved, but device complexity and cost increase due to requiring specialized hardware
Solution Approach 1:
The patent replaces specialized hardware mechanisms with software-based solutions. Instead of using expensive hardware timestampers and precision timing circuits, the invention uses software libraries to capture high-resolution timestamps from commodity clock sources and applies algorithmic corrections for network delay variations. This substitution achieves nanosecond-level accuracy using standard off-the-shelf components.
Solution Approach 2:
The patent introduces an intermediary coordination server that manages the synchronization process. This server collects timing data from multiple machines, calculates optimal offset corrections, and distributes adjustment parameters back to the machines. The intermediary handles the complex calculations and coordinate management, allowing individual machines to use simple commodity clocks while achieving synchronized nanosecond-level timing across the distributed system.
3Device complexity
If commodity clocks are used without specialized equipment, then device complexity is reduced, but clock frequency stability deteriorates due to environmental factors
Solution Approach 1:
The patent implements continuous feedback mechanisms where machines periodically exchange timing messages and measure round-trip delays. The coordination server analyzes this feedback data to detect frequency drift and offset variations, then calculates corrective adjustments. This closed-loop feedback system compensates for environmental factors affecting commodity clocks, maintaining synchronization accuracy despite frequency instability.
Solution Approach 2:
The patent employs dynamic adjustment of clock offsets and frequencies based on real-time network conditions and observed drift patterns. Rather than using static synchronization values, the system continuously adapts timing parameters to account for changing environmental conditions, network load variations, and clock drift. This dynamic approach allows commodity clocks to maintain nanosecond-level synchronization despite inherent frequency instability.
Data Source
AI summary
Systems and methods are disclosed herein for syntonizing machines in a network. A coordinator accesses probe records for probes transmitted at different times between pairs of machines in the mesh network. For different pairs of machines, the coordinator estimates the drift between the pair of machines based on the transit times of probes transmitted between the pair of machines as indicated by the probe records. For different loops of at least three machines in the mesh network, the coordinator calculates a loop drift error based on a sum of the estimated drifts between pairs of machines around the loop and adjusts the estimated absolute drifts of the machines based on the loop drift errors. Here, the absolute drift is defined relative to a drift of a reference machine.