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Financial Data Center Configuration Management System Based on Random Forest Algorithm and Few-Shot Learning.

Xinxin Li1, Lina Wang2

  • 1Capital University of Economics and Business, Beijing 100070, China.

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This study introduces a new detection method, RFCO (random forest algorithm based on clustering optimization), and a target matching algorithm using few-shot learning (FSL). These methods improve infrastructure management and problem resolution in data centers.

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Area of Science:

  • Computer Science
  • Data Management
  • Machine Learning

Background:

  • Data centers face challenges in managing complex infrastructures due to decentralized operation and maintenance models.
  • Ineffective configuration management hinders rapid problem identification and resolution during infrastructure failures.

Purpose of the Study:

  • To develop an integrated financial center configuration management system (FCCMS).
  • To propose advanced algorithms for efficient infrastructure management and fault detection in data centers.

Main Methods:

  • A detection method, RFCO (random forest algorithm based on clustering optimization), was developed, selecting optimal trees from a random forest (RF) for integration.
  • A target matching algorithm based on few-shot learning (FSL) was studied, applying target detection models for matching and positioning tasks using machine learning (ML).

Main Results:

  • The proposed RFCO method enhances detection effectiveness by integrating optimized random forest trees.
  • The FSL-based target matching algorithm demonstrates efficacy in target identification and localization tasks.
  • Experimental validation on relevant datasets confirms the algorithm's effectiveness across various scenarios.

Conclusions:

  • The developed FCCMS aims to unify configuration and information management for improved data center operations.
  • The novel RFCO and FSL-based algorithms offer significant improvements in infrastructure management, fault detection, and problem resolution within data centers.