MAPS Framework Automates Storage System Test Data Collection
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Solution Overview
Problem
Existing methods for testing and analyzing resource consumption and performance in data storage systems are inefficient, error-prone, and time-consuming, particularly when executing data storage system management commands that create or delete multiple logical units (LUNs), as they often require manual scripting and analysis, which limits reusability and introduces human error.
Innovation Solution
A testing and data collection framework, referred to as the MAPS (Measuring Automated Performance Statistics) framework, that systematically measures and collects various indicators and metrics during test execution, automatically generates reports, and supports pluggable data collectors, tests, and report generators, enabling automated analysis and archiving of test data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scripting and analysis methods are used for testing data storage system management commands, then flexibility in customization is maintained, but productivity is reduced and human error increases
Solution Approach 1:
The testing framework is segmented into distinct modular components: test case definitions, data collection modules, analysis engines, and report generation systems. Each component can be independently configured and executed, allowing high productivity through automation while managing complexity through clear separation of concerns.
Solution Approach 2:
The framework provides universal testing capabilities that can handle multiple types of data storage system management commands (create LUN, delete LUN, etc.) through a unified automation interface. This multi-functionality increases productivity across different testing scenarios while the standardized structure manages the inherent complexity.
2Reliability
If manual testing methods are used, then implementation simplicity is maintained, but reliability is reduced due to human error
Solution Approach 1:
The automation framework performs self-service through automated execution of test cases, automatic collection of performance data, automated analysis of results, and automated report generation. This eliminates human error entirely while the structured modular design manages the complexity of the automated systems.
Solution Approach 2:
The framework implements continuous feedback loops where test execution results automatically trigger data collection, which feeds into analysis engines that generate reports and can trigger subsequent test cases. This automated feedback mechanism ensures high reliability by eliminating manual intervention errors while the systematic feedback structure manages complexity.
3Productivity
If automated data collection is implemented, then productivity is improved, but device complexity increases due to framework architecture
Solution Approach 1:
The data collection functionality is segmented into specialized collectors for different resource types (CPU collectors, memory collectors, storage collectors, etc.). Each collector is independently configured and executes specific data collection tasks, achieving high productivity through specialized automation while managing complexity through functional segmentation.
Solution Approach 2:
The framework dynamically configures and executes data collection based on test case requirements. Collectors are activated, configured parameters are adjusted, and data collection intensity is adapted based on the specific testing scenario being executed. This dynamic behavior increases productivity by collecting only necessary data while managing complexity through on-demand configuration.
4Measurement precision
If detailed reporting and historical analysis are implemented, then measurement precision is improved, but loss of time increases for report generation
Solution Approach 1:
The framework performs preliminary actions by continuously collecting and archiving performance data during test execution and between test runs. This pre-collected data is stored in structured formats that enable rapid retrieval and analysis. The precision of measurements is maintained through continuous collection, while report generation time is reduced by having data pre-organized and ready for analysis.
Solution Approach 2:
The framework creates structured copies of performance data in standardized formats during data collection, separating raw data from analysis requirements. This copying approach maintains measurement precision by preserving original data integrity while enabling fast report generation through pre-processed data structures that can be quickly queried and rendered without re-analyzing raw data.
Data Source
AI summary
Techniques for testing may include: receiving a first command at a framework, wherein the first command includes first information identifying a first test to be executed and a first collector that collects data during execution of the first test; and responsive to receiving the first command, perform processing to execute the first command, wherein the processing is coordinated by the framework and wherein the processing includes: executing the first test; collecting test data, wherein the test data is collected by the first collector during execution of the first test; and generating a report regarding the test data collected while executing the first test.