Automatic Sensor Tuning for Log Analytics
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Solution Overview
Problem
Current log analysis systems lack the ability to dynamically tune sensors to optimally select and collect relevant data, leading to inefficiencies in data collection and analysis, particularly in responding to alert conditions such as errors or warnings, where additional contextual information is needed but not captured.
Innovation Solution
A method is introduced that generates a piped HTTP request for analytics on machine data in a search cluster, processes the response to determine a new configuration, and executes a sync instruction to replace the old configuration, allowing the collector to collect event-specific data relevant to the alert condition, thereby dynamically adjusting data collection based on analytics output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensors collect all machine data from enterprise components, then complete data coverage is achieved, but data collection efficiency deteriorates due to collecting unnecessary data
Solution Approach 1:
The sensor configuration is made dynamic by automatically adjusting it based on alert conditions. When an alert is triggered, the system dynamically modifies the sensor configuration to collect additional relevant data, transforming a static collection approach into an adaptive one that responds to system state changes.
Solution Approach 2:
The system changes the parameters of data collection by modifying sensor configurations based on analytics results. When specific alert conditions are detected, the sensor parameters are adjusted to collect different types or amounts of data, optimizing collection efficiency for the current system state.
2Productivity
If sensors are configured to collect event-specific data for alert conditions, then data collection efficiency improves, but data coverage completeness deteriorates
Solution Approach 1:
The system implements a feedback loop where analytics results from collected data are used to automatically adjust sensor configurations. This closed-loop approach ensures that data collection is optimized based on actual system needs identified through continuous analysis, balancing efficiency and completeness.
Solution Approach 2:
The system performs preliminary data collection with a baseline configuration, then uses analytics on this initial data to determine what additional data should be collected. This preliminary action allows the system to adapt its collection strategy based on early findings rather than requiring complete prior knowledge.
3Measurement precision
If manual sensor configuration is used to optimize data collection, then data collection precision improves, but system complexity increases due to manual tuning requirements
Solution Approach 1:
The system performs self-service by automatically configuring sensors based on its own analytics results. The analytics engine analyzes collected data and automatically adjusts sensor configurations without external intervention, enabling the system to optimize its own data collection precision while reducing operational complexity.
4Adaptability or versatility
If dynamic sensor tuning is implemented based on analytics, then adaptability improves, but processing time increases due to continuous configuration adjustments
Solution Approach 1:
The system implements periodic action by triggering sensor configuration changes only when specific alert conditions are met, rather than continuously adjusting configurations. This event-driven approach maintains adaptability while minimizing the time overhead associated with configuration changes.
Data Source
AI summary
A process for automatic tuning a set of collectors and/or sensors includes: collecting first machine data by a first sensor in a collection framework, processing the first machine data by a first collector in the collection framework to yield first collected machine data, performing analytics on the first collected machine data to generate analytics output, and tuning, based, at least in part, on the analytics output, at least one of the following: the first sensor and the first collector.