Distributed Trace Data Analysis Agents for Behavior Verification
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Solution Overview
Problem
Conventional distributed tracing systems can only analyze a small subset of trace data due to resource constraints, leading to potential overlooks of invalid application behavior.
Innovation Solution
Implementing trace data collection and analysis agents within each computing container to analyze and annotate trace data locally, allowing for efficient detection of service-level failures and distributing the analysis across the distributed application, with annotated data being further analyzed by a central service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trace data is collected and analyzed centrally without distributed agents, then system complexity is reduced, but measurement precision and reliability of behavior verification deteriorate due to resource constraints limiting analysis to a small subset of trace data
Solution Approach 1:
The patent divides the centralized trace data analysis function into distributed segments by deploying trace data collection and analysis agents within individual computing containers. Each agent independently collects and analyzes trace data from its local service, enabling parallel processing across multiple containers. This segmentation allows comprehensive analysis of all trace data without overwhelming a single central system, thereby improving measurement precision while managing complexity through distributed architecture.
Solution Approach 2:
The patent transitions from a single-dimensional centralized analysis model to a multi-dimensional distributed analysis model by introducing spatial distribution across multiple computing containers. Trace data analysis occurs simultaneously in different dimensional spaces (each container's local environment), with results aggregated to form a comprehensive verification picture. This dimensional expansion enables thorough analysis without concentrating all computational burden in one location.
2Reliability
If trace data is analyzed at the service level within computing containers, then reliability of service behavior detection improves, but device complexity increases due to additional agents and distributed architecture
Solution Approach 1:
The patent implements self-service by enabling each computing container to autonomously collect and analyze its own trace data through local agents. Each service monitors and verifies its own behavior without requiring external intervention, improving detection reliability through immediate local analysis. The agent within each container acts as a self-monitoring component that independently validates service behavior against expected patterns.
Solution Approach 2:
The patent establishes feedback loops where trace data collection and analysis agents continuously monitor service behavior, compare it against expected patterns, and generate alerts or annotations when anomalies are detected. This real-time feedback mechanism improves reliability by immediately identifying service behavior failures. The feedback flows from local analysis results to centralized aggregation, creating a closed-loop verification system.
Data Source
AI summary
Systems and methods for distributed tracing of a distributed application include collecting and analyzing trace data within computing containers providing services of the distributed application. Failures of a service to conform to a behavior policy can be immediately flagged, and in some examples this may trigger further analysis by a trace data collection and analysis service. By analyzing trace data at the service level within the computing containers providing the services, failures of a service to conform to a behavior policy can be detected quickly and efficiently.