Backup System Risk Score via Predictive Model
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Solution Overview
Problem
Current data backup and recovery systems lack an effective mechanism to monitor and assess the overall protection health over time, relying on rudimentary analysis techniques and manual configuration, which makes it difficult to account for changing dynamics and provide a comprehensive system assessment.
Innovation Solution
A system that generates a data protection risk assessment score using a predictive model based on historical data, automatically configuring thresholds and identifying anomalous behavior, providing a single overall risk assessment score that accounts for changes over time and simplifies monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based policies and static manually defined thresholds are used for monitoring, then the system provides basic protection failure detection, but the system requires manual configuration and adjustment, reducing ease of operation
Solution Approach 1:
The monitoring system automatically learns baseline performance metrics and dynamically adjusts thresholds without manual intervention. The system self-configures by analyzing historical data patterns and adapting to changing system dynamics, eliminating the need for administrators to manually define and update monitoring policies.
Solution Approach 2:
The system transitions from static manually-defined thresholds to dynamic adaptive thresholds that automatically adjust based on observed system behavior patterns. The monitoring parameters evolve over time to reflect changing system conditions, maintaining detection accuracy without requiring manual reconfiguration.
2Reliability
If comprehensive monitoring of all system components is implemented, then the overall protection health can be assessed, but the system complexity increases
Solution Approach 1:
The system consolidates monitoring data from multiple individual components into a unified risk assessment score. By aggregating and synthesizing information from various system elements, the system provides comprehensive oversight through a single integrated view, reducing the complexity of managing numerous separate monitoring mechanisms.
Solution Approach 2:
The monitoring system serves multiple functions through a unified framework: it detects protection failures, assesses overall system health, identifies anomalies, and provides risk scoring. This multi-functional approach eliminates the need for separate specialized tools for each monitoring task, simplifying the overall system architecture.
3Ease of manufacture
If static monitoring thresholds are used, then the configuration is simple, but the system cannot account for changing dynamics over time
Solution Approach 1:
The system performs preliminary learning during an initialization phase, establishing baseline performance metrics and patterns before entering production monitoring. This preliminary action enables the system to adapt to changing dynamics automatically, maintaining both simplicity and adaptability without requiring complex real-time configuration adjustments.
Solution Approach 2:
The system continuously monitors actual system performance and uses this feedback to automatically adjust monitoring thresholds and detect anomalies. The feedback loop enables the system to adapt to changing dynamics in real-time, maintaining detection accuracy while preserving configuration simplicity through automated self-adjustment.
Data Source
AI summary
Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an “at-a-glance” overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators.