Streaming Anomaly Detection for Distributed Application Troubleshooting
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Solution Overview
Problem
Modern distributed computing systems face challenges in real-time detection and troubleshooting of performance issues due to the overwhelming volume of log messages and metric data, leading to delayed identification of problems and their sources, which can result in prolonged service interruptions and increased costs.
Innovation Solution
The development of computational methods and systems that detect anomalous behavior in distributed applications by monitoring streams of metric data and log messages, identifying the approximate time of anomaly occurrence, and providing administrators with insights to determine the root cause, enabling timely remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical management systems use rudimentary rule and threshold-based approaches to detect problems, then the systems can operate with simple detection logic, but they are not able to provide meaningful real time insight into when a problem occurred and the source of the problems
Solution Approach 1:
The patent introduces an intermediary anomaly detection system that sits between the raw metric streams and the administrators. This system processes and analyzes metric streams using sophisticated algorithms to identify anomalies, their timing, and potential sources, thereby providing precise detection without requiring administrators to directly handle the complexity of raw data analysis.
Solution Approach 2:
The patent replaces simple rule-based mechanical detection systems with advanced analytical systems that use machine learning and statistical methods. These systems automatically analyze metric streams, identify patterns, and detect anomalies with high precision, substituting sophisticated computational mechanisms for simple threshold-based rules.
2Measurement precision
If administrators sort through vast numbers of log messages and metric data to determine when the problem started and the source of the problem, then they can identify the root cause, but this process delays troubleshooting and execution of remedial measures
Solution Approach 1:
The patent performs preliminary analysis of metric streams continuously in the background, so that when an anomaly occurs, the system has already prepared information about the anomaly's characteristics, timing, and potential sources. This preliminary action eliminates the need for administrators to manually search through data after a problem occurs, significantly reducing troubleshooting time while maintaining accurate root cause identification.
Solution Approach 2:
The system provides immediate feedback to administrators when anomalies are detected, including information about when the problem started and its likely source. This feedback mechanism delivers precisely the information administrators need without requiring them to manually analyze data, thereby reducing troubleshooting time while maintaining accurate problem identification.
3Speed
If the system monitors streams of metric data and log messages for indications of anomalous behavior, then real-time detection of problems is achieved, but the volume of data to be processed increases significantly
Solution Approach 1:
The patent extracts only the relevant and anomalous information from vast streams of metric data and log messages. Instead of processing and presenting all raw data, the system identifies and extracts only the portions that indicate anomalies, their timing, and potential sources. This extraction approach enables real-time detection while managing data volume by focusing only on critical information.
Solution Approach 2:
The system segments the continuous streams of metric data into manageable analysis units and processes them incrementally. By dividing the data stream into segments and analyzing them in real-time batches, the system achieves fast detection speeds while processing data in manageable portions rather than overwhelming volumes all at once.
Data Source
AI summary
Computational methods and systems for detecting and troubleshooting anomalous behavior in distributed applications executing in a distributed computing system are described herein. Methods and systems discover nodes comprising the application. Anomaly detection monitors the metrics associated with the nodes for anomalous behavior in order to identify an approximate point in time when anomalous behavior begins to adversely impact performance of the application. Anomaly detection also monitors logs messages associated with the nodes to detect anomalous behavior recorded in the log messages. When anomalous behavior is detected in either the metrics and/or the log messages an alert identifying the anomalous behavior is generated. Troubleshooting guides an administrator and/or application owner to investigate the root cause of the anomalous behavior. Appropriate remedial measures may be determined based on the root cause and automatically or manually executed to correct the problem.