Root Causes Prediction in Data Center Using Convolutional Dense Neural Network

Jeremy Filbert Baskoro, Fidelio Soares De Carvalho, Catur Apriono

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Rapid advances in telecommunications, media, information technology and the widespread development of global information infrastructure have changed the patterns and ways of doing business in industry, commerce, and government. A reliable data center must support these industries. However, a data center with a complex network architecture can cause problems or system failures, such as database issues, memory, and network response. This research proposed a root cause prediction in a data center using a combination of CNN and DNN, considering the dataset provided by an open-source dataset. This research also considers zero padding and dropouts to avoid overfitting and adds convolutional layers to improve accuracy. The matrix confusion and the training accuracy are above 90% and up to 82 %, respectively. These results indicate that the proposed model can provide a reliable root cause prediction in a data center.

Original languageEnglish
Title of host publication2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-10
Number of pages4
ISBN (Electronic)9798350363104
DOIs
Publication statusPublished - 2024
Event10th International Conference on Smart Computing and Communication, ICSCC 2024 - Bali, Indonesia
Duration: 25 Jul 202427 Jul 2024

Publication series

Name2024 10th International Conference on Smart Computing and Communication, ICSCC 2024

Conference

Conference10th International Conference on Smart Computing and Communication, ICSCC 2024
Country/TerritoryIndonesia
CityBali
Period25/07/2427/07/24

Keywords

  • CNN
  • deep learning
  • DNN
  • forecasting
  • root cause

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