Predictive Data Archiving for Unplanned Outage Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data backup systems fail to adequately protect against unplanned outage conditions, leading to potential data loss due to insufficient backup processes being triggered too late or not at all.
Innovation Solution
Implementing an AI engine that predicts outage conditions using real-time and historical data, takes control of backup processes to execute archiving in a compressed format when an outage is imminent, and returns control to the database management unit once the outage ends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional backup systems are used, then data is archived regularly, but data loss occurs during unplanned outage conditions because backup processes are not triggered in time
Solution Approach 1:
The AI engine performs preliminary analysis of system patterns and predicts outages before they occur, enabling the backup system to execute archiving operations in advance of actual outages. This predictive capability transforms reactive backup into proactive data protection, ensuring backups are completed before system failures occur.
Solution Approach 2:
The system continuously monitors system performance metrics and feedback loops, using this information to refine outage predictions and adjust backup timing dynamically. The AI engine learns from historical data and real-time system state to improve prediction accuracy and optimize backup execution timing.
2Reliability
If backup processes are executed frequently to ensure data safety, then data protection is improved, but system resources and processing time are consumed unnecessarily during normal operation
Solution Approach 1:
The backup system transitions from static scheduled execution to dynamic predictive execution. The AI engine continuously analyzes system state and adjusts backup timing based on predicted outage probability, executing backups only when necessary rather than following rigid schedules. This dynamic approach optimizes the balance between data protection and system efficiency.
Solution Approach 2:
The system changes the parameter of backup execution timing from fixed intervals to variable intervals based on predicted system state. By adjusting when backups occur based on AI predictions of outage likelihood, the system avoids unnecessary backups during stable operation while ensuring protection before predicted outages.
3Speed
If manual backup control is maintained by database management units, then system simplicity is preserved, but rapid response to outage conditions cannot be achieved
Solution Approach 1:
The AI engine acts as an intermediary layer between the database management unit and backup processes. It receives system state information, predicts outages, and autonomously triggers backup operations without requiring complex manual control logic in the database management unit. This intermediary approach enables rapid automated response while keeping the core database system relatively simple.
Solution Approach 2:
The backup system becomes self-service through AI-driven autonomous operation. The AI engine automatically monitors system state, predicts outages, and executes backup processes without human intervention or complex manual control mechanisms. This self-service capability enables rapid response to predicted outages while avoiding the complexity of manual control systems.
Data Source
AI summary
A computer-implemented method of data archiving is provided. The computer-implemented method includes predicting when an outage condition of a computing system is likely to occur, taking over control of data archiving from a database management unit of the computing system and archiving data prior to the outage condition taking effect and returning control of the data archiving to the database management unit following termination of the outage condition.