Content-Based Dataset Tagging for Multi-Network Data Protection
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Solution Overview
Problem
Existing data management systems struggle to scale with the exponential growth of large and dynamic datasets across diverse network environments, requiring location-specific attention and configurations, which is inefficient and impractical for managing data lifecycles.
Innovation Solution
A large-scale data management system that implements content-based data protection by grouping datasets based on metadata, allowing centralized management across disparate networks without location-specific configurations, using datasets as a logical unit for data protection policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional location-specific data management is used, then each location can be managed independently with local control, but the system cannot scale to handle exabyte-scale data across multiple locations efficiently
Solution Approach 1:
The patent implements a universal data management system that handles data across multiple locations (edge devices, core networks, cloud networks) through a single centralized interface. The system provides location-agnostic data protection, security, and lifecycle management, eliminating the need for location-specific configurations while maintaining the ability to manage data at any scale from GB to exabyte levels.
Solution Approach 2:
The system segments data management into independent modular components: data ingestion modules at various locations, a centralized management plane, and distributed execution engines. This segmentation allows each component to operate independently while contributing to the overall scalable architecture, enabling the system to handle multiple data sources simultaneously without increasing complexity.
2Productivity
If manual data management approaches are used, then detailed control over each data item is possible, but the process cannot keep up with increasing data volumes and rates
Solution Approach 1:
The system implements self-service automation where the data management platform automatically discovers data across locations, classifies it using machine learning, applies appropriate lifecycle policies, and executes protection and security measures without human intervention. This automated self-service capability enables the system to process exabyte-scale data at high throughput while reducing the time required for manual data management tasks.
Solution Approach 2:
The system incorporates continuous feedback loops that monitor data characteristics, access patterns, and policy compliance across all locations. This feedback enables dynamic adjustment of data lifecycle management, allowing the system to automatically optimize throughput and respond to changing data conditions in real-time, thereby increasing productivity while minimizing management time.
3Ease of operation
If location-specific configurations are required for each network environment, then local optimization is achieved, but the system becomes restrictive and difficult to manage across diverse environments
Solution Approach 1:
The patent creates a universal data management platform that operates consistently across diverse network environments including edge devices with limited resources, core networks with high throughput, and cloud networks with elastic scaling. The system automatically adapts to each environment's characteristics while maintaining a single unified configuration model, eliminating the need for location-specific setups and simplifying operations across all deployments.
Solution Approach 2:
The system dynamically adjusts its operation based on the characteristics of each network environment it encounters. It automatically detects whether it is operating at an edge device, core network, or cloud platform and optimizes its behavior accordingly, all while maintaining a consistent universal interface for users. This dynamic adaptation enables ease of operation without sacrificing environment compatibility.
4Reliability
If centralized data management is implemented, then consistent policies can be applied across all locations, but the system requires complex configurations for each location to work properly
Solution Approach 1:
The patent implements a universal configuration model where a single data protection policy definition is automatically applied across all locations and network environments. The centralized management system handles the complexity of location-specific requirements internally, allowing administrators to define policies once and have them consistently enforced across edge devices, core networks, and cloud platforms without requiring location-specific configuration adjustments.
Data Source
AI summary
Providing content-based protection and process control to data objects in a multi-network system, by scanning content data to identify metadata associated with data objects to be protected by a defined protection policy. The content data is stored in storage devices comprising network attached storage (NAS), object storage, local storage, or cloud networks, and where the storage devices are deployed in different networks including core networks, edge networks, and cloud networks. The gathered metadata is stored in a catalog, and a user entered query is executed against the catalog to generate a dataset. The defined protection policy is then applied to the dataset to operate on the corresponding data objects referenced by the dataset as a single unit based on data content rather than data location in a file directory or physical location within the multi-network system.