Dynamic Computer Activity Monitoring with Adaptive Data Collection
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Solution Overview
Problem
Existing methods for collecting data from instrumented software are inflexible and resource-intensive, limiting the types of information that can be collected and requiring re-instrumentation for changes, which is impractical and resource-consuming.
Innovation Solution
A dynamic data tracking mechanism that allows an initial minimal set of information to be collected, with the ability to switch to a more extensive set based on performance evaluation, using a persistent agent to process and report events, thereby minimizing resource usage when performance is within acceptable ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all desired information is tracked continuously, then measurement precision and diagnostic capability are improved, but resource consumption increases
Solution Approach 1:
The monitoring system dynamically adjusts the level of detail in data collection based on performance conditions. When performance issues are detected, the system transitions from collecting minimal information to collecting extensive diagnostic information, and vice versa when performance is acceptable. This dynamic adaptation resolves the contradiction by making the measurement precision variable rather than fixed.
Solution Approach 2:
The system changes the parameter of information collection depth based on performance thresholds. When performance metrics fall outside acceptable ranges, the system increases the amount and detail of information collected. When performance is within acceptable ranges, it reduces collection to minimal essential data, thereby optimizing resource usage while maintaining diagnostic capability when needed.
2Measurement precision
If extensive information is tracked continuously, then diagnostic capability is improved, but device complexity increases
Solution Approach 1:
The monitoring system is segmented into different operational modes: a normal mode that collects minimal essential information and a diagnostic mode that collects extensive detailed information. This segmentation allows the system to maintain low complexity during normal operation while having the capability to switch to high diagnostic capability when performance issues are detected, without requiring the full complexity to be active continuously.
3Use of energy by moving object
If minimal information is tracked, then resource consumption is reduced, but diagnostic capability deteriorates
Solution Approach 1:
The system employs feedback mechanisms where performance metrics are continuously monitored and fed back to control the level of information collection. When performance remains within acceptable thresholds, minimal information collection is maintained to conserve resources. When performance deviates from acceptable ranges, the feedback triggers a switch to extensive information collection, ensuring diagnostic capability is activated precisely when needed rather than operating at minimal capability continuously.
Data Source
AI summary
A mechanism of monitoring activity on a computer which may be applied to measuring the performance of the computer. The computer is configured to track a first set of information relating to at least a first occurrence of at least one scenario on the computer. At least some of the first set of information is evaluated to make a determination about the first occurrence of the scenario. Based on that determination, the computer may be configured to track a second set of information relating to at least a second occurrence of the scenario on the computer, in which the second set of to information includes at least some types of information not tracked in the first set of information. The second set of information can then be evaluated.