Synchronous Asynchronous Monitoring Engine Anomaly Detection
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Solution Overview
Problem
Distributed network systems face challenges in detecting and addressing anomalies, such as slower response times and inaccessibility, due to the difficulty in monitoring and identifying security vulnerabilities, code issues, and surges in user activity at the application layer.
Innovation Solution
A method and system utilizing asynchronous and synchronous monitoring engines to identify anomalies by collecting metrics across the distributed computing environment, triggering synchronous data collection when necessary, and using a detection engine to compare characteristics with stored fingerprints for anomaly identification and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If asynchronous monitoring is used to collect metrics across the distributed computing environment, then the monitoring coverage and anomaly detection capability are improved, but the system complexity and resource consumption increase
Solution Approach 1:
The monitoring system is segmented into two distinct components: an asynchronous monitoring engine that continuously collects metrics with high coverage, and a synchronous monitoring engine that performs detailed analysis only when anomalies are detected. This segmentation allows the system to maintain broad monitoring coverage while reducing overall complexity by limiting intensive processing to only when necessary.
Solution Approach 2:
The system implements periodic action by using asynchronous monitoring to continuously gather metrics at low intensity, then switching to synchronous monitoring periodically or event-driven when anomaly indicators are detected. This periodic intensification of monitoring effort improves anomaly detection capability while avoiding continuous high-resource consumption.
2Measurement precision
If synchronous monitoring is triggered based on asynchronous data analysis, then the precision of anomaly identification is improved, but the response time and processing delay increase
Solution Approach 1:
The asynchronous monitoring engine performs preliminary action by continuously collecting and pre-analyzing metrics data, identifying potential anomalies before they become critical issues. This preliminary detection allows the synchronous monitoring to be triggered only when necessary, reducing overall processing delay while maintaining high identification precision through targeted analysis.
Solution Approach 2:
The asynchronous monitoring engine acts as an intermediary between continuous data collection and detailed synchronous analysis. It filters and pre-processes the incoming metrics stream, identifying candidate anomalies that then trigger synchronous monitoring. This intermediary layer reduces the frequency of expensive synchronous operations while maintaining high detection precision.
3Reliability
If both asynchronous and synchronous monitoring engines are deployed, then the comprehensive monitoring capability is improved, but the system resource consumption and operational complexity increase
Solution Approach 1:
The system merges asynchronous and synchronous monitoring engines into a unified monitoring platform with a shared data infrastructure and coordinated operation. The asynchronous engine continuously collects metrics while the synchronous engine performs detailed analysis when triggered, with both sharing the same anomaly database and reporting mechanisms. This merging provides comprehensive monitoring capability while managing operational complexity through integrated architecture.
Solution Approach 2:
The monitoring system implements self-service through automated anomaly detection and classification. The asynchronous engine automatically identifies potential anomalies from continuous metrics collection, triggers synchronous monitoring when needed, and the system automatically correlates findings without requiring manual intervention. This automation maintains comprehensive monitoring capability while simplifying operation by eliminating manual monitoring tasks.
Data Source
AI summary
A system and method for synchronous and asynchronous monitoring of network resources is provided. In an example system, an asynchronous monitoring engine is in communication with the network resources and receives asynchronous data from a portion of the network resources. The asynchronous data is collected at an application layer or at an end-user application layer. A synchronous monitoring engine is also in communication with the network resources and accesses synchronous data in the application layer in response to receiving a request from the asynchronous monitoring engine. The system further identifies an anomaly corresponding to the asynchronous characteristic and the synchronous characteristic.