Adaptive Threshold Setting for SLO Violation Prediction
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Solution Overview
Problem
Conventional computer systems face challenges in setting and managing component performance thresholds effectively, particularly in correlating these thresholds with service-level objectives (SLOs), leading to sub-optimal prediction of system-level problems in complex systems with multiple tiers and interdependencies.
Innovation Solution
A method and system for automatically and adaptively setting component thresholds by monitoring and recording threshold and SLO violations, constructing a predictive model using historical data to estimate false-positive and false-negative rates, and iteratively updating threshold values using logistic regression to converge to desired accuracy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static threshold settings are used, then system management is simple, but prediction accuracy of SLO violations deteriorates
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously learning from historical violation data and adapting threshold values based on changing system conditions. The system transitions from static, manually-configured thresholds to dynamically adjusted thresholds that automatically respond to system behavior patterns, thereby improving prediction accuracy without requiring proportional increases in operational complexity.
Solution Approach 2:
The system performs self-configuration by automatically learning optimal threshold values from historical data and adjusting thresholds without manual intervention. The automated threshold adjustment mechanism enables the system to self-optimize its monitoring parameters, reducing the need for expert configuration while improving detection accuracy.
2Adaptability or versatility
If manual threshold configuration is used, then implementation is simple, but adaptability to changing system conditions deteriorates
Solution Approach 1:
The system performs preliminary learning during an initialization phase, analyzing historical violation data before actual monitoring begins. This preliminary action allows the system to pre-establish optimal threshold values based on learned patterns, enabling rapid adaptation to new system conditions without requiring extensive manual configuration time when deployment occurs.
Solution Approach 2:
The system continuously monitors actual violation patterns and uses this feedback to iteratively refine threshold values. By incorporating feedback loops that compare predicted versus actual violations, the system automatically adapts to changing conditions over time, achieving high adaptability without requiring manual reconfiguration efforts.
3Reliability
If high thresholds are set to reduce false positives, then alert quality improves, but detection of actual problems deteriorates
Solution Approach 1:
The system applies different threshold values to different system components and conditions based on their specific characteristics and historical behavior patterns. Rather than using uniform high thresholds across all metrics, the system tailors thresholds locally to each component's normal variation patterns, maintaining high alert reliability while preserving sensitivity to actual violations through component-specific threshold optimization.
Data Source
AI summary
A method for managing a computer system includes monitoring first violations of a service level objective (SLO) of a service running on the computer system so as to determine a first statistical behavior of the first violations. Second violations of a component performance threshold of a component of the computer system are monitored so as to determine a second statistical behavior of the second violations. A model that predicts the second statistical behavior based on the first statistical behavior is produced. The component performance threshold is automatically adjusted responsively to the model, so as to improve a prediction of the first violations by the second violations.