Data Storage Insurance Policy Selection Based on File Attributes
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Solution Overview
Problem
Current data storage insurance systems face challenges in efficiently allocating resources for data recovery, as they often rely on manual user assessments of importance, leading to uneven resource distribution and potential high costs for storage and recovery, which can result in providers opting to compensate users instead of investing in storage resources.
Innovation Solution
A method and system that determine data storage insurance policies based on data file attributes such as uniqueness, criticality, and user-defined importance, and hardware attributes like performance tiers, to automatically assess the optimal storage strategy, redundancy, and recovery time, thereby optimizing resource allocation and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user assessment of data importance is used, then users can specify their own priorities, but resource allocation becomes uneven and costs increase
Solution Approach 1:
The system performs automatic data valuation and policy selection without requiring manual user input. The processor autonomously evaluates data files based on extracted attributes (uniqueness, criticality, importance) and selects appropriate insurance policies, eliminating the need for users to manually assess and specify priorities for their data files.
Solution Approach 2:
The system changes the assessment parameters from subjective manual user ratings to objective automated evaluations based on multiple data attributes. By computing uniqueness scores, criticality scores, and importance scores from actual data characteristics rather than user perceptions, the system achieves more accurate and consistent resource allocation.
2Reliability
If high performance storage devices are allocated to all marked important files, then data recovery reliability improves, but available capacity is exhausted and future files cannot be stored properly
Solution Approach 1:
The system applies different storage policies and performance tiers to different data files based on their individually assessed characteristics. Instead of uniformly allocating high-performance storage to all marked files, the processor evaluates each file's uniqueness, criticality, and importance to determine the appropriate insurance policy and storage tier, ensuring that only files that truly require high-performance storage receive such resources.
Solution Approach 2:
The system provides data storage insurance policies with varying degrees of protection and recovery guarantees rather than maximum protection for all files. By offering multiple policy tiers with different recovery scores and performance levels, the system allocates resources proportionally to actual needs, preventing over-allocation while maintaining adequate protection for critical files.
3Reliability
If storage and restoration resources are spent on low-value files, then data recovery capability is maintained, but costs exceed the value of the files
Solution Approach 1:
The system autonomously evaluates each data file's value and determines the appropriate level of insurance coverage and recovery resources required. By automatically computing valuation scores based on data attributes and comparing them against recovery costs, the system eliminates the need for manual cost-benefit analysis and ensures that resources are only allocated to files where recovery is economically justified.
Solution Approach 2:
The system introduces a cost-benefit analysis parameter by comparing the computed data file value (based on uniqueness, criticality, and importance) against the cost of storage and restoration. This parameter change enables the system to make economically informed decisions about resource allocation, ensuring that recovery capabilities are maintained only for files where the value justifies the investment.
4Reliability
If multiple data storage insurance policies are created, then data protection coverage is improved, but policy selection complexity increases
Solution Approach 1:
The system automatically selects the appropriate data storage insurance policy based on the evaluated characteristics of each data file. Instead of presenting users with multiple policy options requiring manual selection, the processor autonomously determines the optimal policy tier by comparing the file's uniqueness, criticality, and importance scores against available policy options, thereby maintaining comprehensive protection while eliminating selection complexity.
Data Source
AI summary
Disclosed herein are systems and method for determining data storage insurance policies. In an exemplary implementation, a method comprises receiving a request to add a data storage insurance policy to a plurality of data files. The method comprises extracting data file attributes and determining a data recovery score for each respective data file based on a uniqueness, criticality, and/or importance of the respective data file. The method comprises determining a hardware score for each of a plurality of performance tiers comprising at least one storage server, based on an available capacity, a performance cost, and/or data recovery scores of data files currently stored at each of the plurality of performance tiers. The method comprises selecting and executing a data storage insurance policy for the respective data file based on a plurality of data recovery rules and/or the comparison of the data recovery score and the hardware score.