TY - GEN
T1 - Root Causes Prediction in Data Center Using Convolutional Dense Neural Network
AU - Baskoro, Jeremy Filbert
AU - De Carvalho, Fidelio Soares
AU - Apriono, Catur
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - CNN
KW - deep learning
KW - DNN
KW - forecasting
KW - root cause
UR - http://www.scopus.com/inward/record.url?scp=85207481219&partnerID=8YFLogxK
U2 - 10.1109/ICSCC62041.2024.10690593
DO - 10.1109/ICSCC62041.2024.10690593
M3 - Conference contribution
AN - SCOPUS:85207481219
T3 - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
SP - 7
EP - 10
BT - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Smart Computing and Communication, ICSCC 2024
Y2 - 25 July 2024 through 27 July 2024
ER -