Network Clock Syntonization Using Loop Drift Correction
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Solution Overview
Problem
Existing clock synchronization technologies struggle to achieve nanosecond-level accuracy without requiring specialized hardware, leading to inefficiencies and fairness issues in networked computer systems due to clock frequency drift.
Innovation Solution
A system and method for clock syntonization using network observations and adaptive stochastic control to estimate and adjust clock offset and frequency drift, utilizing a coordinator to synchronize local clocks to a reference clock with high precision, even with commodity hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hardware is used to achieve nanosecond-level clock synchronization accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces specialized hardware clock synchronization systems with a software-based solution using adaptive stochastic control algorithms. The system uses standard commodity hardware clocks and applies computational methods (Kalman filters, maximum likelihood estimators) to achieve nanosecond-level synchronization accuracy without requiring specialized hardware components throughout the network.
Solution Approach 2:
The patent changes the operating parameters of standard commodity hardware clocks through software control. By continuously estimating clock offset and frequency drift using network observations and applying adaptive corrections, the system transforms ordinary hardware clocks into precision timekeeping devices achieving nanosecond-level accuracy.
2Reliability
If specialized hardware is deployed throughout the network for clock synchronization, then reliability is improved, but ease of manufacture and deployment deteriorate
Solution Approach 1:
The patent creates a universal software solution that works with standard commodity hardware clocks across different platforms and network configurations. The adaptive stochastic control system can be deployed on any machine with a standard clock and network interface, eliminating the need for specialized hardware deployment and making the solution universally applicable throughout the network.
Solution Approach 2:
The system performs self-calibration and self-correction by continuously monitoring network observations and automatically adjusting clock frequency estimates. The adaptive stochastic control algorithms continuously learn from observed clock behavior and autonomously correct synchronization errors without requiring manual intervention or specialized hardware maintenance.
3Device complexity
If millisecond-level synchronization algorithms are used without specialized equipment, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements continuous feedback loops where network observations of clock behavior are constantly monitored, analyzed, and used to adjust frequency drift estimates. The adaptive stochastic control system uses Kalman filters and other estimation algorithms to process feedback from actual clock performance and dynamically correct synchronization errors, achieving nanosecond-level precision with standard hardware.
Solution Approach 2:
The system transitions from static millisecond-level synchronization to dynamic nanosecond-level synchronization by continuously adapting frequency estimates based on real-time network observations. The adaptive stochastic control algorithms dynamically adjust to changing clock behavior, thermal conditions, and network latency variations, maintaining high precision under varying operating conditions.
4Device complexity
If clock frequency drift is not corrected, then device complexity remains low, but stability of timekeeping deteriorates
Solution Approach 1:
The patent implements periodic frequency estimation and correction cycles where the system regularly observes clock behavior, updates frequency drift estimates using adaptive stochastic control, and applies corrections at optimized intervals. This periodic action maintains clock frequency stability by continuously counteracting drift while keeping the system relatively simple and avoiding excessive processing.
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.