@article{MB4E738F4,
title = "Towards Carbon-Neutralization: Deep Learning-Based Server Management Method for Efficient Energy Operation in Data Centers",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2023",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2023.12.4.149",
author = "Sang-Gyun Ma/Jaehyun Park/Yeong-Seok Seo",
keywords = "Data Center, Deep Learning, LSTM, Quality of Service, Server Management, Traffic Prediction",
abstract = "As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center
is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently,
studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from
various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the
server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a
dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The
proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power
used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of
Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data.
Experimental results show that the proposed model is helpful for reliably and efficiently running servers."
}