Fault Tolerance
The current self-correcting algorithm for Agents references the sampling method of RS encoding and performs adaptive training based on it.
Last updated
The current self-correcting algorithm for Agents references the sampling method of RS encoding and performs adaptive training based on it.
Last updated
By introducing machine learning algorithms, polling detection not only relies on scheduled checks but also incorporates anomaly detection models. This allows the system to predict and diagnose potential faults based on historical data.
For smaller-scale systems, polling detection can be set to run every 5 minutes, while for large-scale, high-concurrency systems, it is recommended to conduct checks every 30 seconds to 1 minute.
The initial accuracy rate can reach 90%-95%, and as the model continues to iterate, the precision can gradually increase to 99%.
The Mean Time to Repair (MTTR) should be kept within 5 minutes, with most automatic recovery operations completed within 1 minute.
The task allocation of validators typically relies on consensus mechanisms such as PBFT, Raft, or BFT-SMaRt, ensuring that all validators participate in validating each transaction and data update in each round, maintaining the validity and consistency of the data.
To ensure system reliability and data consistency, the number of distributed validators should be at least five to provide sufficient redundancy and prevent single points of failure. In high-security scenarios, the number of validators can be increased to 10-15 to reduce the risk of malicious attacks through majority validation.
The system utilizes prioritized validator allocation, assigning tasks to healthy and lightly loaded validators to improve accuracy and efficiency. The overall system accuracy and effectiveness should reach 99.9%.
Data sampling validation is performed using Data Availability Sampling, where light nodes and full nodes communicate to verify data consistency. The consistency check is based on the sampled data points, and cryptographic hash algorithms are used to compare the data for consistency. In high-reliability systems, the accuracy of each sampling validation should be maintained at over 99.9%.
The system can sample and validate between 5% and 10% of the data to ensure the representativeness and validity of the system's data. For smaller systems with fewer data points, the sampling rate can be increased to 20%-30% to ensure adequate coverage for validation.
The system will periodically create snapshots to ensure data recoverability. Snapshot frequency is typically set between every 10 to 30 blocks, and the exact frequency should be determined based on the rate of system changes and the importance of the data.
