Profiling Data Snapshots for Software Performance Analysis
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Solution Overview
Problem
In distributed application server environments, performance and scalability issues arise due to concurrent user access, and existing profiling tools struggle to effectively generate and display meaningful snapshots of resource consumption data in real-time, especially when filtering and analyzing large volumes of profiling data.
Innovation Solution
The method involves executing a profiling session on a virtual machine, collecting data over a specified interval, generating snapshots based on user-defined filters and markers, and transmitting this data for display, allowing for logical filtering and reporting intervals to focus on specific aspects of application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive profiling data is collected over a long time interval, then measurement precision and data completeness are improved, but data volume increases making analysis and display difficult
Solution Approach 1:
The patent divides comprehensive profiling data into multiple snapshots representing different time intervals. Each snapshot contains a subset of profiling data for a specific period, allowing users to analyze manageable portions while maintaining the option to view complete data across multiple snapshots. This segmentation resolves the contradiction by making large data volumes tractable without losing measurement precision.
Solution Approach 2:
The system implements periodic snapshot generation at defined time intervals during the profiling session. Instead of continuously processing all profiling data, the system periodically captures state information at interval boundaries, reducing the amount of data that needs to be displayed and analyzed at any one time while maintaining comprehensive coverage through multiple periodic snapshots.
2Productivity
If real-time snapshot generation is implemented, then productivity and responsiveness are improved, but system complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary organization of profiling data during collection, structuring it by time intervals and application components. Snapshot markers are pre-defined at specific time intervals, and data is organized according to these markers during the profiling session. This preliminary organization enables rapid snapshot generation without requiring complex real-time processing, thus improving productivity while controlling system complexity.
Solution Approach 2:
The system extracts only the necessary subset of profiling data for each snapshot based on predefined time intervals and logical filters, rather than processing and displaying all collected data. This extraction approach reduces processing overhead and system complexity by focusing computational resources on generating specific snapshots rather than managing the entire dataset simultaneously.
3Measurement precision
If detailed filtering options are provided, then measurement precision and analysis capability are improved, but ease of operation deteriorates
Solution Approach 1:
The system applies different levels of filtering detail to different users or different analysis scenarios. Basic users can generate snapshots with default filters for ease of operation, while advanced users can access detailed logical filter definitions for precise data analysis. This local quality approach allows the system to provide appropriate complexity levels to different users, maintaining ease of operation for simple tasks while enabling measurement precision when needed.
Solution Approach 2:
The system implements a tiered filtering approach where basic filtering is applied by default to maintain ease of operation, and detailed filtering options are available but not required for basic snapshot generation. Users can apply detailed logical filters when precision is needed, but can operate with partial filtering for routine tasks, thus balancing ease of operation with measurement precision.
4Loss of information
If multiple snapshots are generated and stored, then information retention is improved, but loss of time for data retrieval and processing increases
Solution Approach 1:
The patent stores profiling data as segmented snapshots rather than as a single large dataset. Each snapshot is independently stored with its time interval and associated metadata, allowing the system to retrieve and process only the specific snapshots needed for analysis. This segmentation dramatically reduces data retrieval time compared to processing a single comprehensive dataset, while maintaining complete information retention through the collection of all snapshots.
Solution Approach 2:
The system performs preliminary organization and indexing of snapshots during the profiling session, storing metadata about each snapshot including time intervals, filtered criteria, and data subsets. This preliminary organization enables rapid retrieval of specific snapshots without requiring full data processing, thus reducing data retrieval time while maintaining complete information retention through proper indexing and storage structure.
Data Source
AI summary
Implementations of the present disclosure provide methods including executing a profiling session to profile one or more applications running on a virtual machine executed on a server, collecting profiling data over a first time interval during the profiling session, receiving user input generated at a client device, the client device being in communication with the server, in response to the user input, generating a snapshot corresponding to a subset of the profiling data, the snapshot being identified based on a second time interval that is within the first time interval, and transmitting the profiling data to generate a snapshot view for display on a client device, the snapshot view corresponding to the subset of the profiling data.