APM Server Filtering Logic for Distributed System Monitoring
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Solution Overview
Problem
Current application performance monitoring (APM) systems in distributed computer systems face challenges in effectively collecting and presenting real-time operational status data, leading to inefficiencies in identifying application performance issues and end-user experience impacts.
Innovation Solution
An APM server is configured to receive and apply rules to APM data, determining application operational status based on predefined thresholds and logical conditions, and visualize this status through a graphical user interface, utilizing APM agents and network devices for data collection and filtering logic to prevent false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional APM systems collect and process all APM data without filtering, then complete performance monitoring is achieved, but false positives increase and system complexity increases
Solution Approach 1:
The patent segments the data processing function by introducing filtering logic that separates valid performance issues from false positives. The filtering logic divides the data stream into validated issues and discarded false alarms, improving measurement precision without requiring complete system redesign.
Solution Approach 2:
The filtering logic acts as an intermediary component between data collection and issue identification. It processes raw APM data, applies validation rules, and outputs only confirmed performance issues, thereby improving accuracy while maintaining manageable system complexity through modular architecture.
2Reliability
If APM systems monitor multiple performance parameters in real-time, then comprehensive application status is achieved, but false error identifications increase
Solution Approach 1:
The filtering logic implements feedback mechanisms that evaluate multiple performance parameters together and adjust error identification based on correlated data. When multiple parameters indicate an issue, the system confirms the error; when parameters conflict, the system discards false positives, thereby improving reliability while reducing false alarms.
Solution Approach 2:
The system performs preliminary validation of APM data before final error identification. The filtering logic pre-processes data by applying validation rules and cross-checking multiple parameters, eliminating false positives before they reach the error identification stage, thus improving reliability without increasing false alarms.
3Productivity
If APM systems process all collected data without filtering, then no performance issues are missed, but system resources are wasted on false positives
Solution Approach 1:
The filtering logic extracts and removes false positive data from the APM data stream before further processing. By taking out invalid data points that would otherwise consume computational resources, the system improves productivity by focusing processing power only on genuine performance issues while reducing energy waste on false errors.
Data Source
AI summary
Systems and methods for presenting application performance monitoring data in distributed computer systems. An example method may comprise: receiving, by a processing device, one or more application performance monitoring (APM) rules, each APM rule determining an application operational status based on one or more APM parameters; receiving APM data comprising a plurality of APM data items representing values of the APM parameters of a software application; determining an operational status of the software application by applying the APM rules to the APM data; and causing the operational status to be visually represented via a graphical user interface (GUI).