Issue Shelf-Life Scheduling for Adaptive Telemetry Collection
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Solution Overview
Problem
Traditional methods of collecting telemetry and configuration data in complex computing systems burden resources by treating all issues equally and performing regular data collections, leading to inefficient use of network and compute resources.
Innovation Solution
Adjusting data collection frequency based on the shelf-life of detected issues, calculated using factors such as data change frequency, sampling rate, detectability, self-remediation likelihood, and occurrence probability, to only collect data when the issue is about to expire or has expired.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional comprehensive data collection methods are used to monitor computing system health, then detection reliability is improved, but resource consumption increases
Solution Approach 1:
The patent changes the parameter of data collection frequency from a fixed regular interval to a dynamic interval based on issue shelf-life characteristics. Different issue types are assigned different shelf-life values, which directly control when next data collection occurs. This parameter change allows the system to maintain reliable detection for critical issues while reducing resource consumption for stable issues.
Solution Approach 2:
The system transitions from static periodic data collection to dynamic adaptive data collection. The collection frequency automatically adjusts based on the determined shelf-life of each detected issue, making the monitoring system flexible and responsive to the actual state of the computing system rather than following a rigid schedule.
2Reliability
If regular periodic data collections are performed for all detected issues, then detection completeness is improved, but productivity decreases
Solution Approach 1:
The patent introduces shelf-life as a key parameter that determines data collection timing. By calculating and storing shelf-life values for different issue types, the system can predict when re-detection is necessary, eliminating unnecessary periodic collections and improving overall system productivity while maintaining detection completeness.
Solution Approach 2:
The system performs preliminary determination of issue shelf-life characteristics before executing data collection. This preliminary action allows the system to pre-calculate optimal re-detection intervals, avoiding wasted resource expenditure on unnecessary data collections while ensuring complete detection coverage when actually needed.
3Reliability
If comprehensive data collection is performed frequently to ensure no issues are missed, then detection reliability is improved, but loss of time increases
Solution Approach 1:
The patent changes the time parameter of data collection from fixed periodic intervals to variable intervals based on issue shelf-life. Critical issues with short shelf-lives trigger frequent re-detection, while stable issues with long shelf-lives allow extended intervals, optimizing the balance between detection reliability and time efficiency.
Solution Approach 2:
The system dynamically adjusts data collection timing based on the specific characteristics of each detected issue. Rather than using a uniform time schedule, the system adapts the re-detection interval to match the actual stability and risk profile of each issue, reducing unnecessary time expenditure while maintaining reliable detection.
Data Source
AI summary
Methods are provided for determining decay rates and/or shelf-lives of intellectual capital (IC) detected issues for only performing actions for expired or about to expire IC detected issues. Specifically, the methods involve performing one or more data collections that include data relating to one or more of a configuration of a computing system or an operation of the computing system and detecting an issue in the computing system based on the data. The issue relates to an anomaly in one or more of the configuration of the computing system or the operation of the computing system. The methods further involve determining a shelf-life for the issue, where the shelf-life indicates an estimated duration of the issue existing in the computing system before redetecting whether the issue is still present and adjusting a next data collection of the one or more data collections based on the shelf-life of the issue.