Data Protection Policy Mapping via Cluster Topology Discovery
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Solution Overview
Problem
Existing data protection systems face inefficiencies in mapping protection policies to data clusters due to the complexity of data cluster topologies, leading to suboptimal utilization of computing resources and increased user involvement.
Innovation Solution
A data protection manager with a mapping module that identifies discovery events in data clusters, obtains topology information, maps protection policies using data cluster topology and protection information, and updates policy mappings to initiate data protection services efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping of protection policies to data clusters is performed, then policy mapping accuracy can be maintained, but user involvement increases and productivity decreases
Solution Approach 1:
The system enables self-service by automatically discovering data clusters and their topologies, then autonomously mapping protection policies based on discovered information. The data protection manager performs self-mapping without requiring user intervention, thus maintaining accuracy while improving productivity.
Solution Approach 2:
The patent introduces a mapping module as an intermediary between data clusters and protection policies. This module automatically discovers cluster topologies and performs policy mapping based on discovery events, eliminating the need for manual user involvement while ensuring accurate mappings through systematic processing.
2Productivity
If automated policy mapping is implemented, then productivity improves and user involvement decreases, but device complexity increases
Solution Approach 1:
The system segments the policy mapping process into distinct modular components: a mapping module that handles policy assignments, a discovery module that identifies data clusters, and a topology analysis component. This segmentation manages complexity by organizing functions into separate, manageable modules that can operate independently.
Solution Approach 2:
The system performs preliminary discovery actions to identify data clusters and their topologies before policy mapping occurs. By pre-discovering cluster structures and storing topology information, the system prepares necessary data in advance, simplifying the subsequent policy mapping process and reducing overall system complexity.
3Reliability
If continuous discovery events are monitored, then data protection services remain up-to-date, but use of energy increases
Solution Approach 1:
The system implements periodic action by triggering discovery events and policy mapping operations only when specific events occur, such as when new data clusters are detected or when topology changes are identified. This event-driven periodic monitoring maintains data protection service currency while avoiding continuous monitoring that would consume excessive computing resources.
Data Source
AI summary
Techniques described herein relate to a method for managing data protection services for data clusters. The method includes identifying, by a mapping module of a data protection manager, a first discovery event associated with a first data cluster; in response to identifying the first discovery event: obtaining first data cluster topology information associated with the first data cluster from the first data cluster; obtaining data cluster data protection information from a data cluster data protection information repository; mapping first protection policies to the first data cluster using the first data cluster topology information and the data cluster data protection information; updating data cluster topology information protection policy mappings using the using the first data cluster topology information and the data cluster data protection information; and initiating performance of first data protection services for the first data cluster based on the first mapped protection policies.