Profiling Data Interestingness Rating for Resource Optimization
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Solution Overview
Problem
In complex application server runtime environments, identifying inefficient methods with excessive resource consumption is challenging due to the large number of methods involved and interrelated resource consumption patterns, making simple sorting misleading.
Innovation Solution
A system and method for rating the interestingness of profiling data subsets by assigning scores to nodes based on their individual and combined resource consumption, selecting a representative subset of nodes, and evaluating their interestingness rate to prioritize resource-intensive methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple sorting of methods based on individual resource consumption is used, then the identification process is simple and fast, but the results are misleading and do not accurately identify inefficient methods
Solution Approach 1:
The patent combines individual resource consumption scores with combined resource consumption scores (that account for interrelated methods) into a unified interestingness rate calculation. This merging allows the system to maintain simple sorting operations while incorporating the complexity of method interrelationships, thus preserving identification speed while improving accuracy.
Solution Approach 2:
The patent transforms the single parameter of individual resource consumption into a composite parameter (interestingness rate) that incorporates both individual consumption and combined consumption metrics. This parameter transformation enables more accurate identification of inefficient methods while maintaining the efficiency of sorting-based approaches.
2Measurement precision
If detailed analysis of all thousands of methods is performed to account for interrelated resource consumption, then identification accuracy improves, but the complexity and time required increases significantly
Solution Approach 1:
The patent extracts only the necessary information (individual scores and combined scores) from the full profiling data and uses these extracted metrics to calculate interestingness rates. This extraction approach allows accurate identification without requiring complex analysis of all method interrelationships, thus reducing analysis complexity while maintaining precision.
Solution Approach 2:
The patent applies partial action by using simplified score metrics that capture the essential interrelationships without performing exhaustive analysis of all method connections. This partial approach to accounting for interrelationships reduces complexity while still improving accuracy compared to simple individual sorting.
3Measurement precision
If detailed analysis of all thousands of methods is performed to account for interrelated resource consumption, then identification accuracy improves, but the time required increases significantly
Solution Approach 1:
The patent performs preliminary calculation of combined resource consumption scores that capture method interrelationships in advance. These pre-computed scores are then used in the interestingness rate calculation, allowing the system to account for interrelationships without performing time-consuming analysis during the actual identification process, thus reducing profiling time while maintaining accuracy.
4Ease of operation
If a subset of nodes is selected based on interestingness rate, then the overview becomes clear and manageable, but some resource-intensive methods might be excluded
Solution Approach 1:
The patent uses the interestingness rate parameter that incorporates both individual and combined resource consumption to select representative subsets. This parameter transformation ensures that the selected subsets clearly represent the bulk of resource consumption while maintaining completeness, as the interestingness rate is designed to capture the most significant methods based on their overall impact.
Data Source
AI summary
Systems and methods for rating interestingness of profiling data subsets are described. Profiling data including a set of nodes associated with at least one executed computer program is received. The profiling data contains a first set of scores corresponding to the resource consumption of the nodes. A second set of scores is identified from the profiling data, wherein a score of the second set of scores indicates a combined resource consumption of a corresponding node of the set of nodes and at least one other node of the set of nodes related to the corresponding node. An interestingness rate is evaluated for groups of nodes of the set of nodes based on the corresponding scores. The group of nodes with the highest, or satisfactory high value of the calculated interestingness rate is presented.