Adaptive Cluster Data Protection for Node Role Changes
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Solution Overview
Problem
Multi-node database management systems (DBMS) pose challenges for data storage management systems due to frequent role changes among nodes, requiring manual intervention to ensure timely backups and restores, which is cumbersome, costly, and prone to errors.
Innovation Solution
A data storage management system with enhanced data agents and a pseudo-client entity that automatically adapts to node configuration changes, enabling automated backup and restore operations without human intervention, by determining the principal node and operational nodes, and managing data communication and replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual intervention is used to check and adjust for role changes at the DBMS, then data protection operations can be performed with basic system configuration, but the system requires timely human presence and intervention, causing loss of time and reduced productivity
Solution Approach 1:
The data storage management system automatically detects role changes at the DBMS and reconfigures itself without human intervention. The system monitors the DBMS, identifies when principal/replica roles change, and autonomously updates its configuration to maintain optimal data protection operations, eliminating the need for manual checking and adjustment.
Solution Approach 2:
The system implements continuous monitoring of the DBMS role assignments and uses this feedback to dynamically adjust its data protection configuration. By establishing a feedback loop that detects role changes and triggers automatic reconfiguration, the system adapts to changing conditions while minimizing manual intervention and time loss.
2Productivity
If the data storage management system automatically detects role changes, then productivity and reliability are improved, but the device complexity increases due to additional monitoring and adaptation mechanisms
Solution Approach 1:
The monitoring component of the data storage management system serves multiple functions: it detects role changes, determines current node assignments, and triggers reconfiguration operations. By making this component multi-functional, the system achieves automatic adaptation and improved productivity without proportionally increasing overall system complexity.
Solution Approach 2:
The system combines the role detection, status monitoring, and reconfiguration trigger functions into an integrated automated process within the data storage management system. By merging these functions rather than implementing them as separate systems, the complexity increase is minimized while achieving continuous automatic adaptation to role changes.
3Device complexity
If human administrators manually reconfigure the data storage management system for each role change, then the system structure remains simple, but the ease of operation deteriorates due to cumbersome administrative tasks
Solution Approach 1:
The data storage management system performs self-configuration by automatically detecting role changes at the DBMS and adjusting its own settings without requiring administrator intervention. This self-service capability eliminates cumbersome manual reconfiguration tasks while maintaining a relatively simple system structure.
Solution Approach 2:
The system is pre-configured with the capability to automatically detect and respond to role changes, so that when role changes occur, the system is already prepared to handle them autonomously. This preliminary preparation enables easy operation without requiring complex ad-hoc administrative procedures.
4Extent of automation
If the system requires manual checking of role changes, then automation extent is low, but reliability deteriorates because scheduled backups may fail without human intervention
Solution Approach 1:
The data storage management system autonomously monitors DBMS role assignments and automatically reconfigures itself when changes are detected, eliminating dependence on human intervention. This self-service mechanism ensures that scheduled backups and data protection operations reliably execute regardless of role changes, improving both automation extent and operational reliability.
Solution Approach 2:
The system continuously monitors role assignments and prepares for potential role changes by maintaining the capability to quickly detect and respond to them. This ongoing monitoring and preparedness acts as a cushion against failures, ensuring that backup operations remain reliable even when unexpected role changes occur between scheduled tasks.
Data Source
AI summary
An illustrative data storage management system protects data in a replicated multi-node system (“cluster”). The illustrative system creates a new administrative entity (“pseudo-client”) that represents the cluster within the system. The system intelligently performs as an adaptive “set it and forget it” system. Preferences for the pseudo-client, including alternate rules that cover various contingencies in the cluster, need not be reset as the cluster undergoes node failures and/or role changes over time. Rather, the disclosed system adapts, on each successive secondary copy job, to the current configuration of the cluster without requiring a system administrator to provide further input or analysis. This approach ensures that jobs can begin promptly as scheduled or on demand. Component selection is based at least in part on geography/zone and/or network topology considerations, which improves the likelihood of job completion and success, and RPO/RTO service levels are more likely to be met.