Dynamic Performance Profiling for Enterprise Storage Networks
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Solution Overview
Problem
As storage networks grow, configuring and managing applications and storage subsystems becomes challenging, especially in dynamic environments, as existing methods rely on static data that cannot effectively monitor or analyze conditions, limiting the ability to enhance performance dynamically.
Innovation Solution
A unified enterprise-level server system that collects performance data, generates performance profiles, and applies them to configure applications and storage subsystems based on desired criteria, while monitoring and analyzing performance to ensure optimal operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static data is used for initial setup and configuration, then configuration simplicity is maintained, but the ability to dynamically enhance performance and adapt to environmental changes is lost
Solution Approach 1:
The system transitions from static configuration data to dynamic performance monitoring and analysis. Performance profiles are continuously updated based on real-time data collection from multiple sources (application performance counters, storage subsystem performance counters, host performance counters), enabling the system to adapt to changing environmental conditions while maintaining ease of operation through automated profile generation and application.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting performance data, analyzing it against stored profiles, and using the results to dynamically adjust configurations. The performance monitor module continuously monitors performance attributes and compares them against thresholds, providing feedback that triggers automated configuration adjustments to optimize application and storage subsystem performance.
2Ease of manufacture
If manual or automatic assignment based on static data is used, then initial configuration is achieved, but dynamic monitoring and analysis of application and storage subsystem conditions cannot be performed
Solution Approach 1:
The system enables self-service through automated performance profiling and configuration optimization. The performance profiler automatically collects data from multiple performance counters, generates performance profiles without manual intervention, and applies them to optimize configurations. The system serves itself by continuously monitoring its own performance attributes and making automated adjustments, eliminating the need for ongoing manual configuration while maintaining precise performance monitoring.
3Measurement precision
If a unified enterprise-level system with multiple modules is implemented, then performance monitoring and configuration accuracy are improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: performance data collector engine, performance profiler, performance monitor module, and performance analyzer module. Each module has a specific responsibility (data collection, profile generation, real-time monitoring, and analysis respectively), which simplifies the overall system architecture while maintaining high measurement precision. This modular segmentation allows for independent development, testing, and maintenance of each component.
Solution Approach 2:
The unified enterprise-level system performs multiple functions through its modular architecture: it collects performance data from diverse sources (applications, storage subsystems, hosts), generates performance profiles, monitors real-time performance, and provides automated configuration optimization. This multi-functionality is achieved through a single integrated system that can handle various storage network configurations and performance scenarios, reducing the need for multiple separate tools while maintaining comprehensive monitoring capabilities.
Data Source
AI summary
A unified enterprise level method and system for enhancing a performance of applications and storage subsystems in a storage network are disclosed. In one embodiment, a method for enhancing the performance of the storage network having applications and storage subsystems includes collecting performance data associated with the applications and the storage subsystems, and generating performance profiles for a set of combinations of the applications and the storage subsystems implemented in the storage network based on the performance data. The method also includes receiving desired performance criteria for an application of the storage network, and applying a performance profile to configure the application and a storage subsystem assigned to the application substantially similar to the desired performance criteria.