Dynamic Dataset Chargeback for Content-Based Data Protection
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Solution Overview
Problem
Existing data management systems struggle to handle the explosive growth of large-scale, dynamic datasets, leading to challenges in cost allocation and data lifecycle management, particularly in charge-back and show-back operations, due to the complexity of managing data spread across various locations and formats.
Innovation Solution
Implementing a dataset management system that groups data into logical units based on metadata, allowing for content-based protection policies that span multiple storage devices and environments, using metadata queries to manage and protect data as a unified dataset, rather than focusing on individual assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is managed individually across multiple locations and formats, then detailed control over each data asset is achieved, but the complexity of managing large-scale dynamic datasets increases significantly
Solution Approach 1:
The patent merges data management by creating a unified namespace that consolidates multiple data locations and formats into a single virtualized structure. This allows the system to manage large-scale dynamic datasets as a unified entity rather than individually, reducing management complexity while maintaining control through the unified namespace abstraction layer.
Solution Approach 2:
The unified namespace serves multiple functions simultaneously: it provides a common view of data across locations, enables consistent policy application, supports charge-back operations, and facilitates data protection. This multi-functional approach reduces the need for separate management systems for different data types and locations.
2Reliability
If traditional backup administrator models are used to manage data lifecycles, then detailed policy control is achieved, but the system cannot scale to handle increasing data volumes
Solution Approach 1:
The system implements self-service data lifecycle management where data is automatically tagged with metadata and assigned to appropriate datasets based on predefined criteria. This eliminates the need for manual administrator intervention in routine lifecycle decisions, enabling the system to scale automatically with increasing data volumes while maintaining consistent policy enforcement.
Solution Approach 2:
The data lifecycle management system transitions from static, manually-configured policies to dynamic, automated decision-making based on metadata analysis. The system automatically adapts lifecycle actions based on data characteristics, enabling scalable management of large-scale dynamic datasets without proportionally increasing administrative overhead.
3Measurement precision
If charge-back operations process all disparate financial documents from distributed entities, then accurate cost accounting is achieved, but the processing time and resources required increase significantly
Solution Approach 1:
The system performs preliminary data organization and tagging during data ingestion, preparing financial and operational data for charge-back operations in advance. By pre-organizing data with appropriate metadata and categorization, the system reduces the processing burden during actual charge-back operations, enabling accurate cost accounting without excessive processing delays.
Data Source
AI summary
Performing charge/showback operations in a large-scale data system by scanning multiple data sources to identify data objects for processing as a unitary group with respect to common characteristics that have an impact on finances within the system. Metadata of the identified data objects are stored in a dynamic dataset that defines a single data access unit for the referenced data objects. The system processes a user query regarding cost allocations, cost forecasts, and resource usage of respective groups within an organization. The query initiates a charge-back or show-back operation that allocates costs associated with each respective usage of resources by a department or cost center, and accesses the referenced data objects through the dataset as a single unit based on data content rather than data location in a file directory of the system.