Backup Validation Engine for Policy Conflict Resolution
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Solution Overview
Problem
Current backup processes are inefficient, leading to wasted resources due to duplicate backups, inappropriate backups, and incorrect backup policies, with no integrated solution to address these issues across different data sources and tools, resulting in significant time and cost expenditures.
Innovation Solution
A backup validation engine that integrates data sources, including a central asset repository, network attached storage, and tape backup data, with an interactive user interface and data mapping processor to identify performance issues and automatically initiate corrective actions, such as eliminating duplicate backups and ensuring appropriate backup policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional backup processes are used across multiple data sources, then backup coverage is provided, but resource waste occurs due to duplicate backups and incorrect policies
Solution Approach 1:
The system implements automated feedback loops where backup validation results are continuously monitored and fed back to adjust backup policies. The validation engine compares actual backup outcomes against expected results, and policy modifications are automatically applied based on this feedback, eliminating the need for manual intervention while improving backup accuracy and reducing resource waste.
Solution Approach 2:
The backup validation system performs self-diagnosis and self-correction by automatically detecting duplicate backups, policy conflicts, and coverage gaps. The system autonomously identifies issues across multiple data sources, validates backup integrity, and adjusts policies without requiring external human intervention, thereby reducing operational overhead and resource consumption.
2Measurement precision
If comprehensive backup validation is performed across thousands of assets, then backup accuracy improves, but time and effort requirements increase significantly
Solution Approach 1:
The system performs preliminary validation actions by pre-configuring backup policies with validation rules and pre-identifying potential conflicts before backups execute. Asset inventories are pre-scaned and organized, and validation criteria are pre-established, allowing the system to rapidly validate backups without performing exhaustive checks after each backup operation, thereby reducing validation time while maintaining accuracy.
Solution Approach 2:
The validation system implements periodic validation schedules that adapt to backup frequencies and asset criticality. Instead of validating every backup uniformly, the system performs validations at optimized intervals based on asset importance, backup type, and historical performance, reducing overall validation time while maintaining adequate monitoring of all assets.
3Adaptability or versatility
If multiple independent backup tools are used for different data sources, then specialized backup capabilities are achieved, but integration complexity and cost increase
Solution Approach 1:
The backup validation system is designed as a universal platform that can validate backups from multiple different backup tools and data sources through a single interface. The system employs standardized validation protocols and adaptable data models that work across various backup technologies, eliminating the need for separate validation processes for each tool while preserving the specialized backup capabilities of individual tools.
Solution Approach 2:
The validation engine acts as an intermediary layer between diverse backup tools and the central validation logic. It provides standardized data interfaces and translation mechanisms that allow different backup tools to communicate their status and results to the validation system without requiring direct integration between tools, thereby reducing integration complexity while maintaining tool versatility.
4Ease of operation
If manual monitoring and adjustment of backup policies is performed, then policy customization is possible, but labor costs and human error increase
Solution Approach 1:
The system enables self-service policy management where backup policies are automatically customized based on asset characteristics, business requirements, and validation results. The validation engine autonomously adjusts retention periods, backup frequencies, and storage locations without human intervention, eliminating manual policy configuration while maintaining appropriate customization for different assets and business units.
Solution Approach 2:
The system dynamically changes backup policy parameters based on real-time validation data and asset performance metrics. Instead of manual policy adjustment, the system automatically modifies parameters such as backup schedules, retention periods, and storage allocations in response to changing conditions, maintaining policy appropriateness without requiring human resources.
Data Source
AI summary
An embodiment of the present invention is directed to a backup validation engine. The backup validation engine comprises: a plurality of data sources comprising a central asset repository; a central repository of backups data; a central repository of network attached storage, a central application portfolio repository; and central repository of tape backup data; an interface user interface; and a data mapping processor, coupled to the plurality of data sources and interactive user interface, programmed to: access data from each of the plurality of data sources; map data from the plurality of data sources; identify performance issues comprising: duplicate host policies, hosts back-up, missing backup of hosts, duplicate NAS policies, NAS volume backup, and missing NAS backup; generate a backup validation plan to address one or more performance issues; and automatically initiate the backup validation plan.