Mesh Network Clock Syntonization with Adaptive Drift Control
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Solution Overview
Problem
Current clock synchronization methods, especially in networked systems, face limitations in achieving high accuracy due to frequency drift caused by environmental factors, leading to inefficiencies and fairness issues in applications like finance and distributed databases, often requiring expensive specialized hardware for nanosecond-level synchronization.
Innovation Solution
The system employs a coordinator that uses network observations and adaptive stochastic control to estimate and correct clock frequency drift and offset, ensuring synchronization accuracy within nanosecond-level bounds without the need for additional hardware, using advanced filtering techniques and machine learning models to account for noise and varying clock responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hardware is used throughout the network for clock synchronization, then synchronization accuracy is improved to nanosecond level, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive specialized hardware clocks with inexpensive commodity clocks that can be readily replaced. The system achieves high synchronization accuracy not through hardware quality but through software-based correction algorithms that compensate for the limitations of cheap clocks.
Solution Approach 2:
The patent substitutes hardware-based clock synchronization mechanisms with software-based stochastic control algorithms. Instead of relying on precise mechanical/quartz oscillators, the system uses computational methods including Kalman filters and maximum likelihood estimators to achieve nanosecond-level synchronization accuracy.
2Device complexity
If commodity clocks are used without specialized hardware, then device complexity is reduced, but clock synchronization accuracy deteriorates due to frequency drift
Solution Approach 1:
The patent implements continuous feedback loops where clocks exchange timing information and the stochastic control algorithms constantly adjust frequency offsets based on observed drift. This closed-loop feedback mechanism compensates for the inherent instability of commodity clocks and maintains synchronization accuracy.
Solution Approach 2:
The patent dynamically changes clock frequency parameters through software control. The stochastic algorithms calculate optimal frequency adjustment parameters and apply them in real-time, allowing commodity clocks to be tuned and corrected to achieve precision that would otherwise require specialized hardware.
3Ease of operation
If millisecond-level synchronization is used instead of nanosecond-level, then ease of operation is improved, but productivity and fairness in time-critical applications deteriorate
Solution Approach 1:
The patent enables commodity clocks to self-correct their synchronization errors through automated stochastic control algorithms. The system performs its own calibration and adjustment without requiring specialized hardware or manual intervention, achieving nanosecond-level accuracy that was previously only available through expensive hardware solutions.
Solution Approach 2:
The patent creates a universal synchronization protocol that works across diverse hardware platforms using commodity clocks. The software-based approach is platform-independent and can be deployed throughout the network without requiring specialized hardware, making the solution universally applicable while maintaining high precision.
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.