Data Center Probe Vectors for Network Event Prediction
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Solution Overview
Problem
Managing and optimizing data center operations becomes increasingly complex due to the integration of various technologies and skillsets, making it challenging for operators to monitor and adjust resources effectively in real-time, especially as data centers grow in size and complexity.
Innovation Solution
A system is configured to produce probe vectors from data center hardware instances, which are used to create self-labeled training snapshots that train an inference engine to predict network conditions and events, simplifying data center management by aggregating and interpreting vast amounts of data from multiple components across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data center size and complexity grow to support more application workflows, then service capacity increases, but operational complexity and difficulty of management increase exponentially
Solution Approach 1:
The patent introduces an intermediary system comprising probes, collectors, and machine learning models that mediate between the complex data center infrastructure and human operators. Probes collect raw data from numerous components, collectors aggregate and normalize this data, and machine learning models interpret patterns to generate actionable insights, thereby simplifying operational management as data center complexity grows
2Loss of information
If all data from hardware resources, controllers, orchestrators, and applications are aggregated into a large database, then complete information is available, but interpretation becomes exponentially challenging
Solution Approach 1:
The patent replaces manual data interpretation (mechanical human analysis) with automated machine learning systems. Machine learning models trained on historical data automatically detect patterns, anomalies, and correlations in the aggregated data, substituting human cognitive effort with computational algorithms that can process large datasets efficiently and consistently
Solution Approach 2:
The system enables self-service through automated monitoring and alerting where the data center infrastructure monitors itself. Probes continuously collect data, machine learning models automatically analyze patterns and detect anomalies, and the system generates its own insights without requiring constant human intervention, allowing the system to serve its own management needs
3Ease of operation
If human operators manually monitor and interpret data from multiple functional areas, then real-time management is possible, but scalability is limited by human capacity
Solution Approach 1:
The patent substitutes human operational capacity with automated computational systems. Machine learning models process and interpret data at speeds and scales beyond human capability, enabling real-time management of increasingly complex data centers without being constrained by human cognitive limits or availability
Data Source
AI summary
A method is provided to produce training data set for training an inference engine to predict events in a data center comprising: producing probe vectors corresponding to components of a data center, each probe vector including a sequence of data elements, one of the probe vectors indicating an event at a component and at a time of the event; and producing at a master device a set of training snapshots, wherein each training snapshot includes a subsequence of data elements that corresponds to a time increment that matches or that occurred not later than the indicated time of occurrence of the event.