Automated Storage I/O Impact Analysis After Reconfiguration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed storage systems face challenges in determining the impact of IO performance changes after reconfigurations such as code upgrades or system modifications, making it difficult to assess performance improvements or degradations.
Innovation Solution
Implementing a system that collects and processes performance measurement data before and after configuration changes to determine the positioning of performance metrics, allowing for automated actions based on these changes, including alerts or additional reconfigurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated performance measurement and analysis is implemented, then performance impact determination accuracy is improved, but system complexity increases
Solution Approach 1:
The storage system performs self-measurement and self-diagnosis by automatically collecting performance metrics, comparing them before and after reconfiguration, and determining performance impacts without requiring external manual testing or complex external measurement systems
Solution Approach 2:
The system automatically measures performance, compares pre- and post-reconfiguration metrics, and uses this feedback to determine performance impacts and trigger automated actions, creating a closed-loop system that continuously monitors and adapts
2Loss of time
If manual performance testing is performed, then measurement simplicity is maintained, but measurement time and productivity are reduced
Solution Approach 1:
The system automatically collects and stores performance measurement data before reconfiguration occurs, so that when reconfiguration happens, the baseline data is already ready for immediate comparison, eliminating the need for manual pre-testing and reducing overall measurement time
Solution Approach 2:
The performance measurement and monitoring continues uninterrupted during and after reconfiguration, with the system continuously collecting metrics and automatically comparing them against baseline data, eliminating the downtime and interruptions associated with manual testing procedures
3Reliability
If comprehensive performance monitoring is implemented, then performance analysis accuracy is improved, but operational complexity increases
Solution Approach 1:
The system automatically performs comprehensive performance monitoring, data collection, metric comparison, and impact determination without requiring manual operational intervention, making the complex monitoring process transparent and operationally simple while maintaining high analysis accuracy
Data Source
AI summary
A method in an illustrative embodiment comprises collecting performance measurement data for processing of input-output operations sent by a host to a storage system for a first period of time prior to a configuration change and a second period of time subsequent to the configuration change, processing performance measurement data for the first period of time to determine a first point in a plane defined by a first performance metric and a second performance metric, processing performance measurement data for the second period of time to determine a second point in the plane defined by the first and second performance metrics, determining one or more characteristics of a positioning of the second point relative to the first point, and controlling execution of at least one automated action relating to at least one of the host and the storage system based at least in part on the one or more characteristics.