Data Protection Resource Allocation by Data Criticality
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Solution Overview
Problem
Existing data protection systems lack an adaptive mechanism to allocate resources based on the criticality of data objects, leading to a high risk of critical data being overlooked due to insufficient resource allocation.
Innovation Solution
A data protection system utilizing supervised learning processes, such as KNN-based classification, to tag clients and data objects based on criticality, enabling intelligent resource allocation that prioritizes critical data assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are allocated uniformly across all data objects, then resource management is simple, but critical data objects may not receive sufficient resources for protection
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on data criticality levels. Critical data objects receive higher resource allocation with priority scheduling and dedicated resources, while non-critical objects receive standard allocation. This selective differentiation ensures reliable protection for critical data without uniformly increasing complexity across the entire system.
Solution Approach 2:
The system implements dynamic resource allocation that adapts to changing data criticality requirements. The resource allocation mechanism dynamically adjusts based on real-time assessment of data objects, allowing the system to respond to varying protection needs while maintaining manageable complexity through automated adaptation rather than static complex configurations.
2Productivity
If manual resource allocation is used, then system complexity is low, but resource allocation efficiency and adaptability are poor
Solution Approach 1:
The system employs self-service mechanisms where data objects automatically receive appropriate resource allocation based on their criticality characteristics. The automated assessment and allocation process eliminates manual intervention while maintaining system manageability through rule-based decision making, thereby improving efficiency without proportionally increasing operational complexity.
Solution Approach 2:
The patent utilizes parameter changes by varying resource allocation parameters (such as priority levels, resource quotas, scheduling weights) based on data criticality parameters. This approach enables efficient automated allocation by dynamically adjusting numerical parameters rather than requiring complex structural changes, improving productivity while keeping the allocation mechanism relatively simple.
3Reliability
If all data objects are treated equally, then policy implementation is straightforward, but critical data may be overlooked or assigned lesser resources
Solution Approach 1:
The patent applies segmentation by dividing data objects into distinct criticality categories (critical, important, normal). Each segment receives tailored protection policies and resource allocation appropriate to its category. This segmentation ensures critical data is not overlooked while maintaining straightforward policy management through clear categorical distinctions rather than complex individualized rules.
Data Source
AI summary
The backup schedule for a group of routine or known clients and data objects is generally defined by default backup policies and priorities, and such policies and data assets demand resources of the system. The resource allocation among data assets being processed is analyzed and reallocated (tuned) as necessary to ensure that critical data assets are provided with the necessary resources for their protection operations. Embodiments use a machine learning (ML) model that would identify the resource consumption for the all the running policies to smartly allocate the optimal number of resources based on the criticality of the data object and their policies.