Predicting Latency in Streaming Infrastructure Using ML
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Solution Overview
Problem
Current systems face challenges in managing latency across integrated Message Oriented Middleware (MOM) platforms, leading to negative user experiences and business impacts, as existing methods struggle to effectively predict and prevent latency issues in distributed data streaming and event management infrastructures.
Innovation Solution
The implementation of machine learning-based models that analyze performance metrics from data streaming infrastructures, comprising message producers, brokers, and consumers, to predict and prevent latency-related anomalies by identifying potential issues before they occur, using techniques such as random forest and logistics regression models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple integrated MOM platforms are used for data streaming and event management, then system functionality and versatility are improved, but latency prediction and performance management become more difficult
Solution Approach 1:
The patent segments the complex multi-platform MOM system into individual observable components (message queues, producers, consumers, brokers) and measures performance metrics for each segment separately. This allows latency to be tracked and predicted at the component level rather than attempting to measure the entire integrated system as a monolith, resolving the contradiction between maintaining versatile multi-platform functionality and enabling effective latency detection.
2Device complexity
If traditional monitoring methods are used for integrated MOM platforms, then system complexity is maintained at current levels, but latency prevention capability is insufficient
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict latency issues before they actually occur. The system analyzes historical performance metrics and patterns to forecast potential latency problems in advance, allowing proactive remediation actions to be taken. This transforms the system from reactive monitoring to predictive prevention, improving reliability without requiring fundamental changes to the underlying MOM platform architecture.
3Reliability
If machine learning models are implemented for latency prediction, then latency prevention capability is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that sit between the raw performance metrics collection and the latency prediction output. These ML models act as mediators that consume multiple input metrics from the MOM platforms and translate them into predictive insights about future latency conditions. This intermediary layer manages the complexity by encapsulating the computational requirements within dedicated prediction services rather than embedding complexity throughout the entire MOM infrastructure.
Data Source
AI summary
A method includes receiving a plurality of performance metrics from a data streaming infrastructure. The data streaming infrastructure comprises one or more message producers, one or more message brokers and one or more message consumers. In the method, the plurality of performance metrics are analyzed in a machine learning model, and one or more performance issues with the data streaming infrastructure are predicted based on the analyzing.