Sang-Gyun Ma, Jaehyun Park, Yeong-Seok Seo

KIPS Transactions on Software and Data Engineering, Vol. 12, No. 4, pp. 149-158, Apr. 2023
https://doi.org/10.3745/KTSDE.2023.12.4.149,   PDF Download:   Full Text
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.

Statistics
Cite this article