Rule Book Retention Engine for Dynamic Backup Policies
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Solution Overview
Problem
Current data protection systems inadequately determine retention periods for heterogeneous enterprise data, leading to inefficient storage utilization, potential data loss, and increased costs due to static retention policies that fail to adapt to changing data conditions.
Innovation Solution
A retention management engine that dynamically adjusts retention policies based on multiple factors, including data source, type, storage type, backup frequency, and workload, using a rule-book system that learns from historical data to recommend optimal retention periods and adapt to real-time changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static retention policies are applied to all data, then storage capacity is over-utilized and costs increase, but data loss risks also increase due to improper retention periods
Solution Approach 1:
The system transitions from static retention policies to dynamic retention policies that automatically adjust based on real-time data characteristics, workload patterns, and storage conditions. The retention management engine continuously monitors multiple factors and modifies retention periods accordingly, ensuring optimal balance between storage efficiency and data protection without manual intervention.
Solution Approach 2:
The system changes the retention period parameter dynamically based on multiple input factors including data source type, data category, backup frequency, workload characteristics, and storage capacity. Each factor contributes to adjusting the retention parameter to achieve the optimal balance between minimizing storage costs and preventing data loss.
2Adaptability or versatility
If manual retention policy configuration is used, then policies can be customized per data type, but the complexity and time required for configuration increases significantly
Solution Approach 1:
The retention management engine implements self-service functionality by automatically analyzing data characteristics, workload patterns, and storage conditions to generate and adjust retention policies without requiring manual configuration. The system serves itself by making intelligent decisions based on monitored parameters, eliminating the need for administrators to manually configure complex retention policies for each data type.
Solution Approach 2:
The system incorporates feedback mechanisms where the retention management engine continuously monitors data sources, backup operations, and storage conditions, then uses this feedback to automatically adjust retention policies. The feedback loop enables the system to learn from actual data patterns and refine retention decisions over time without manual intervention.
3Ease of operation
If linear decision-making approaches are used for retention policies, then configuration is simpler, but the system cannot adapt to multi-dimensional factors and changing conditions
Solution Approach 1:
The system transitions from single-dimensional linear decision-making to multi-dimensional policy optimization by considering multiple factors simultaneously including data source type, data category, backup frequency, workload characteristics, storage capacity, and recovery requirements. Each factor represents a dimension that the retention management engine evaluates to determine optimal retention policies.
Data Source
AI summary
Systems and methods for determining retention periods or policies for backups are disclosed. A rule book stores relationships between rules and recommended retention periods. Data related to a backup is collected and organized. A query is generated from the organized data and used to identify a rule from the rule book. The retention period corresponding to the identified rule in the rule book is then applied to the corresponding backup.