Copy Service Parameter Tuning for Distributed Site Recovery
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Solution Overview
Problem
Existing disaster recovery systems in multi-site data storage environments face inefficiencies due to the need for ongoing manual adjustments of numerous parameters, which are interdependent and require significant manual intervention to maintain efficient performance.
Innovation Solution
A method involving a fitness function and genetic algorithm is employed to automatically optimize parameter settings by evaluating different configurations, selecting optimal parameters, and storing them in a configuration parameter table, which is then deployed to production machines for efficient data mirroring operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustments of parameters are performed to maintain efficient performance in disaster recovery systems, then performance can be optimized, but significant manual labor and time are required
Solution Approach 1:
The system performs self-optimization of parameters through automated testing and evaluation. The fitness function automatically evaluates different parameter configurations and selects optimal settings without requiring manual intervention, allowing the disaster recovery system to maintain itself autonomously
Solution Approach 2:
Parameter optimization is performed in advance through automated testing in a test environment before deployment to production. The system pre-evaluates multiple parameter configurations and stores optimal settings in a configuration parameter table, so that when disaster recovery operations are needed, the system is already optimized and ready
2Reliability
If numerous parameters are manually configured and adjusted, then system performance can be maintained, but the complexity of manual intervention increases
Solution Approach 1:
The system automatically manages the complexity of numerous parameters through self-service mechanisms. The fitness function and automated testing process handle parameter evaluation and optimization without requiring human operators to understand or manually adjust each parameter, effectively hiding the complexity from users
Solution Approach 2:
The system systematically explores and adjusts multiple parameters through automated testing. By using a fitness function to evaluate different parameter configurations and automatically selecting optimal values, the system manages parameter complexity through structured parameter changes rather than manual configuration
3Productivity
If automated testing and optimization processes are implemented, then manual labor is reduced and speed is increased, but computational resources and testing time are required
Solution Approach 1:
The system performs parameter optimization in advance in a test environment before production deployment. By conducting automated testing and evaluation beforehand, the system prepares optimal configurations that can be quickly deployed, reducing the time needed for manual adjustment when actually needed
Solution Approach 2:
The system uses a test environment as a copy of the production environment to perform automated testing. By creating and evaluating parameter configurations in a replicated test setting, the system can optimize parameters without affecting production operations, and then deploy the optimized configurations to production
Data Source
AI summary
A computer-implemented method (CIM), according to one embodiment, includes defining a fitness function for testing different configurations in a distributed site environment, and generating a configuration table that includes all possible configurations of a system in a test environment. The method further includes performing a testing process for each of the configurations. The testing process includes initializing a population of a plurality of individuals. The testing process further includes using the fitness function to evaluate a fitness of each of the individuals, selecting a subset of the individuals to be in a parent group, creating new individuals by combining information from multiple individuals in the parent group, and storing refined parameters of the configuration, that are determined to be optimal, to a configuration parameter table. A production machine is caused to tune to the refined parameters of the configuration parameter table for operations performed within the distributed site environment.