17985987. AUTOMATIC FAILOVER OF A NON-RELATIONAL DATABASE simplified abstract (Capital One Services, LLC)

AUTOMATIC FAILOVER OF A NON-RELATIONAL DATABASE

Organization Name

Capital One Services, LLC

Inventor(s)

Maqbool A Khatri of Glen Allen VA (US)

Guganathan Sellamuthu of Glen Allen VA (US)

AUTOMATIC FAILOVER OF A NON-RELATIONAL DATABASE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17985987 titled 'AUTOMATIC FAILOVER OF A NON-RELATIONAL DATABASE

Simplified Explanation

Automatic Failover for Non-Relational Databases

• Machine learning model generated and trained with historical failover data
• Monitoring of non-relational databases for real-time data
• Prediction of primary region failure likelihood
• Automatic designation of new primary node when failure likelihood meets threshold
• Transfer of data services to new primary node in non-failing region

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1. 1. 1. Potential Applications

• Cloud computing
• Data centers
• Disaster recovery systems

1. 1. 1. Problems Solved

• Minimizing downtime in non-relational databases
• Improving data availability and reliability
• Enhancing system resilience against failures

1. 1. 1. Benefits

• Increased system reliability
• Reduced manual intervention for failover processes
• Improved data accessibility and continuity

1. 1. 1. Potential Commercial Applications
         1. Optimizing Data Center Operations with Automatic Failover for Non-Relational Databases

1. 1. 1. Possible Prior Art

No prior art known at this time.

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1. 1. 1. 1. Unanswered Questions

1. 1. 1. How does the machine learning model handle different types of failure scenarios in non-relational databases?

The machine learning model is trained with historical failover data related to various failure scenarios, allowing it to adapt and predict primary region failure likelihood based on different factors.

1. 1. 1. What measures are in place to ensure the security and integrity of data during the failover process?

Security protocols and data encryption methods can be implemented to safeguard data during the failover process, ensuring that data remains secure and intact throughout the transition.

Original Abstract Submitted

Disclosed embodiments pertain to automatic failover for non-relational databases. A machine learning model can be generated and trained with historical failover data. The historical failover data can be related to previous primary node failures or previous region failures of a plurality of non-relational databases. A non-relational database may be monitored for real-time or near-real-time data. The data can be input into the machine learning model to predict the likelihood that a primary region is in a failure state. A new primary node can be designated automatically when the likelihood satisfies a predetermined threshold. Data services are thus automatically transferred from a primary node in the failing region to a new primary node in a non-failing region.