Autonomic Self-Optimization for Protection Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Installation and maintenance of data protection systems in complex networked systems are complex and often result in non-optimal configurations, which can be further disrupted by changing network conditions.
Innovation Solution
An autonomic self-optimization system for protection storage that monitors performance indications and applies configuration changes based on stored rules to ensure optimal data protection, including bandwidth optimization, block size adjustments, and replication management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration of data protection system is performed, then initial setup is completed, but the system configuration becomes non-optimal and requires continuous manual adjustments
Solution Approach 1:
The system implements self-service through an autonomic self-optimization mechanism that automatically monitors performance indications and applies configuration changes based on stored rules, eliminating the need for continuous manual adjustments and enabling the system to adapt autonomously to changing network conditions
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring performance indications from the data protection system and using this information to dynamically adjust configuration parameters, creating a closed-loop control system that maintains optimal performance despite changing conditions
2Productivity
If the data protection system configuration is optimized for initial conditions, then performance is maximized initially, but the configuration becomes suboptimal when network conditions change
Solution Approach 1:
The system transitions from static configuration to dynamic configuration by implementing real-time monitoring of performance indications and automatically adjusting configuration parameters based on current network conditions, allowing the system to maintain optimal performance as conditions change
Solution Approach 2:
The system achieves adaptability by changing configuration parameters dynamically based on monitored performance indications and stored optimization rules, allowing the same system to operate optimally under different network conditions without manual reconfiguration
3Reliability
If complex manual optimization is attempted, then optimal configuration may be achieved, but the complexity and effort of installation and maintenance increases significantly
Solution Approach 1:
The system eliminates complex manual optimization efforts by implementing self-service capabilities that automatically monitor performance and apply configuration changes based on stored rules, maintaining high reliability while removing the burden of complex manual management from operators
Solution Approach 2:
The system introduces an intermediary optimization layer that sits between the raw configuration and the performance outcome, using stored optimization rules and performance monitoring to automatically mediate configuration adjustments, thereby simplifying the overall system while maintaining reliability
Data Source
AI summary
A network system comprises a first component, a second component, and a system for self-optimization. The system for self-optimization comprises an interface and a processor. The interface is configured to receive a performance indication from the first component. The processor is configured to determine a configuration change for the second component according to a set of configuration rules.