Dynamic Sampling Policy Manager for Distributed Tracing

Solution Overview

Problem

Distributed tracing in cloud environments faces challenges with traditional methods, such as inaccurate latency statistics due to discarding important outliers in head-based sampling and increased memory load in tail-based sampling, which affects debugging and monitoring efficiency.

Innovation Solution

A system that uses a policy manager within a modified proxy to determine if a user request matches error response policies, instructing services to collect tracing data in a head-based fashion when errors occur, thereby sending data directly to the tracing server without local storage, enhancing performance and reducing memory load.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If head-based sampling is used for distributed tracing, then the system performance is improved and memory load is reduced, but the accuracy of latency statistics deteriorates due to discarding important outliers

Engineering Contradiction:

Improvesystem performanceVSAvoidlatency statistics accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements dynamic sampling rate adjustment based on detected error patterns. When error responses are detected matching generated policies, the sampling rate increases to capture more error-related transactions for analysis. When no errors are detected, the sampling rate decreases to reduce memory load and improve performance. This dynamic adaptation resolves the contradiction by optimizing the trade-off between capturing sufficient error data and maintaining system performance.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the sampling parameter dynamically based on error detection. By monitoring error responses and adjusting the sampling rate parameter accordingly, the system captures more detailed tracing data when errors occur (improving measurement precision) while maintaining lower sampling rates during normal operation (maintaining productivity). This parameter adaptation allows the system to resolve the contradiction between accuracy and performance.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If tail-based sampling is used for distributed tracing, then the accuracy of detecting anomalistic behavior is improved, but the memory load increases affecting system performance

Engineering Contradiction:

Improveanomalistic behavior detection accuracyVSAvoidsystem performance

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent implements dynamic sampling rate adjustment based on detected error patterns. When error responses are detected matching generated policies, the sampling rate increases to capture more error-related transactions for analysis. When no errors are detected, the sampling rate decreases to reduce memory load and improve performance. This dynamic adaptation resolves the contradiction by optimizing the trade-off between capturing sufficient error data and maintaining system performance.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the sampling parameter dynamically based on error detection. By monitoring error responses and adjusting the sampling rate parameter accordingly, the system captures more detailed tracing data when errors occur (improving measurement precision) while maintaining lower sampling rates during normal operation (maintaining productivity). This parameter adaptation allows the system to resolve the contradiction between accuracy and performance.

Inventive Principle:

Principle #35Parameter changes

3Quantity of substance

If head-based sampling is used, then memory load is reduced, but the ability to trace anomalistic requests deteriorates

Engineering Contradiction:

Improvememory loadVSAvoidanomalistic request information

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The patent implements a feedback mechanism where error responses from services are monitored and used to generate policies that trigger increased sampling. When anomalistic behavior or errors are detected, the system receives feedback about the anomaly type and adjusts subsequent sampling to capture more relevant requests. This feedback loop ensures that memory resources are allocated efficiently while still capturing sufficient anomalistic request information for debugging and analysis.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent implements dynamic sampling rate adjustment based on detected error patterns. When error responses are detected matching generated policies, the sampling rate increases to capture more error-related transactions for analysis. When no errors are detected, the sampling rate decreases to reduce memory load and improve performance. This dynamic adaptation resolves the contradiction by optimizing the trade-off between capturing sufficient error data and maintaining system performance.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11210156B1Intelligent distributed tracing

Publication Date: 2021.12.28 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An approach for distributed tracing is provided. The approach for distributed tracing comprises receiving a user request for a transaction by one or more processing units, and extracting feature data from the received user request by one or more processing units. Further, if the extracted feature data matches at least one policy generated based on at least one error response received from at least one service is determined by one or more processing units and the at least one service is instructed to collect tracing data for the transaction in a head-based fashion responsive to the extracted feature data matching at least one policy by one or more processing units.