GRU Network Generator for Data Center Telemetry Reconstruction

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Solution Overview

Problem

Existing data center monitoring and management systems face challenges in efficiently processing and analyzing large volumes of data center telemetry data from numerous assets, which can lead to delays in detecting operational issues and predicting faults.

Innovation Solution

The method involves receiving data center telemetry data from multiple assets, compressing it using appropriate algorithms, and then reconstructing it using a gated recurrent unit (GRU) network generator for analytics purposes, enabling efficient data processing and fault prediction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data center telemetry data is compressed to reduce data volume, then data processing efficiency is improved, but data reconstruction accuracy may deteriorate

Engineering Contradiction:

Improvedata processing efficiencyVSAvoiddata reconstruction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

A GRU-based neural network generator is introduced as an intermediary component between the compressed data and the reconstruction process. The generator takes compressed telemetry data as input and produces reconstructed data that maintains high fidelity to the original, effectively mediating the trade-off between compression and accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically adjusts compression parameters and GRU network configuration parameters based on data characteristics and required accuracy levels. By changing these parameters, the system can optimize the balance between processing efficiency and reconstruction accuracy for different operational scenarios

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If all data center telemetry data is processed in real-time, then monitoring accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvemonitoring accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts and processes only the most critical features and anomalies from the telemetry data using the GRU generator, rather than processing all raw data. This selective extraction maintains monitoring accuracy while significantly reducing computational complexity

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The monitoring system is segmented into multiple components: data collection, compression, GRU-based reconstruction, and analysis. Each component handles a specific aspect of the data flow, making the overall system more manageable and less complex while maintaining comprehensive monitoring capability

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12224916B2Data center monitoring and management operation including data center analytics data cluster reconstruction operation

Publication Date: 2025.02.11 DELL PROD LP

AI summary

A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center telemetry data; compressing the data center telemetry data to provide compressed data center data; and, performing a data center analytics reconstruction operation, the data center analytics reconstruction operation reconstructing the compressed data center data to provide decompressed data center telemetry data, the data center analytics reconstruction operation using a gated recurrent unit (GRU) network generator.