Risk Prediction System for Proactive Data Center Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data protection systems often reactively address disasters, leading to downtime and data loss, as they lack proactive measures to predict and mitigate adverse events affecting data centers.
Innovation Solution
A computer-implemented method and system that uses big data analytics to predict risks of adverse events by gathering and analyzing data from various sources, allowing for dynamic and automatic adjustments to minimize impact before the event occurs, such as failover operations and data replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive data protection systems are used to address disasters, then data protection can be provided, but downtime and data loss occur due to lack of proactive measures
Solution Approach 1:
The system performs preliminary actions by predicting adverse events before they occur and automatically executing protective measures such as failover operations and data replication. The risk prediction module analyzes data sources to forecast potential disasters, and the system proactively migrates data or switches operations to backup systems before the actual adverse event, thereby eliminating downtime and preventing data loss.
2Reliability
If reactive data protection systems are used, then data protection is provided, but data loss occurs due to lack of proactive mitigation
Solution Approach 1:
The system executes preliminary protective actions by predicting adverse events and automatically replicating data to backup systems before the adverse event occurs. The risk prediction module identifies potential threats and triggers data replication operations in advance, ensuring that data is already safeguarded when the adverse event strikes, thereby preventing data loss.
Solution Approach 2:
The system applies preliminary anti-action by predicting adverse events and executing counter-measures such as failover to backup systems before the adverse event can cause data loss. The risk prediction module forecasts potential disasters and the system proactively switches operations to protected systems, preventing the harmful effect of data loss before it can occur.
3Reliability
If big data analytics are used to predict adverse events, then proactive data protection is enabled, but system complexity increases
Solution Approach 1:
The system introduces an intermediary risk prediction module that uses big data analytics to forecast adverse events. This intermediary component processes data from multiple sources, analyzes risk patterns, and provides predictions to the main system. The intermediary layer simplifies the overall architecture by centralizing the complex analytical functions, allowing the main system to focus on executing protective actions based on predictions.
Data Source
AI summary
A computer-implemented method is provided. First information is received from at least a first data source. Based at least in part on analysis of the received first information, a determination of a first risk of a first adverse event is made, the risk affecting a first entity associated with a first location. Based at least in part on the first risk, at least a first impact from the first adverse event on the first entity is determined. At least a first action is dynamically caused to occur either before the completion of the first adverse event, the first action configured to substantially mitigate the first impact.