Dynamic Request Sampling and Tracing for Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Tracing every request in computing systems becomes computationally infeasible as the number of requests grows, leading to wasted computing resources and reduced data availability for anomaly detection, which can exacerbate system issues.
Innovation Solution
A request processing system (RPS) selectively samples and traces the most beneficial requests, reallocating resources to identify and prevent the spread of processing issues, and adjusts sampling rates based on performance thresholds to enhance data collection and troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If sampling is used to trace requests, then computing resources are saved, but data availability for anomaly detection is reduced
Solution Approach 1:
The sampling rate is made dynamic rather than static. The system automatically adjusts the sampling rate based on real-time system conditions, increasing it when anomalies are detected and decreasing it during normal operation. This resolves the contradiction by adapting data collection intensity to actual needs, ensuring sufficient data availability when required while conserving resources during normal times.
Solution Approach 2:
The system implements feedback mechanisms where tracing results and system performance metrics are continuously monitored and fed back into the sampling decision-making process. When anomalies are detected in traced requests, the system increases sampling rates to gather more data for analysis. This feedback loop ensures that data availability is maintained at appropriate levels while avoiding wasteful tracing during normal operation.
2Loss of information
If sampling rate is increased to improve data availability, then anomaly detection capability is improved, but computing resources are wasted on non-problematic requests
Solution Approach 1:
The sampling rate dynamically adjusts based on system state, transitioning from low sampling during normal operation to high sampling when anomalies are detected. This prevents continuous high-resource consumption while ensuring sufficient data collection when needed most.
Solution Approach 2:
The system changes the sampling rate parameter based on detected system conditions. When performance thresholds are violated or anomalies are detected, the sampling rate parameter is increased. When system health is good, the parameter is decreased. This parameter adaptation resolves the contradiction between data availability and resource consumption.
3Loss of information
If tracing is performed on all requests, then data availability is maximized, but system throughput is reduced
Solution Approach 1:
The tracing operation is made dynamic rather than continuous. The system enables full tracing only when anomalies are detected and uses reduced sampling during normal operation. This dynamic tracing strategy maintains data availability for anomaly investigation while preserving system throughput during normal processing.
Solution Approach 2:
Instead of tracing all requests (excessive action), the system applies partial tracing through sampling. When system conditions are normal, only a subset of requests are traced, maintaining adequate data for anomaly detection while minimizing impact on throughput. When anomalies occur, tracing is intensified to provide sufficient data for resolution.
Data Source
AI summary
System, method, and various embodiments for a request processing system are described herein. An embodiment operates by determining a first sampling rate for each of multiple different types of requests being processed by a first computing landscape, and detecting a change in a performance of a first type of request. The first sampling rate for the first type of request is increased, and a second sampling rate for the first type of request for a second computing landscape responsive is also increased. Results of the tracing corresponding to the increased sampling rates are aggregated, and one or more differences between the first computing landscape and the second computing landscape are identified. An alert to reduce processing of the first type of request is generated based on the aggregated results and the identified one or more differences.