@article{oai:ipsj.ixsq.nii.ac.jp:00240712,
 author = {Naoya, Yokoyama and Kiyofumi, Tanaka and Naoya, Yokoyama and Kiyofumi, Tanaka},
 issue = {11},
 journal = {情報処理学会論文誌},
 month = {Nov},
 note = {When offering services such as e-commerce on the cloud, there is a necessity to adjust the amount of server resources provided in response to the irregularly increasing and decreasing traffic. This need arises when there is a desire to maintain a constant level of service while, at the same time, minimizing costs as much as possible. There is an abundance of prior research regarding the prediction of future loads from past time-series data. Many of these approaches rely on traditional time-series forecasting, which necessitates that the data used for learning adhere to stationary or unit root processes, or they use deep learning approaches that include using a vast amount of data and parameters. In this study, we propose a traffic forecasting method that includes dynamic window size changes that can follow even slight variation in trends. This method incorporates a fuzzy-entropy-based burst traffic detection in the regression estimation with sliding-window learning. In the evaluation, we conduct four comparative experiments using actual traffic rather than simulations. As a result, compared to the baseline, the proposed reduced the number of request failures and improved the Mean Squared Error between the ideal and the actual container count by 26.4 points on average.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.32(2024) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.32.916
------------------------------, When offering services such as e-commerce on the cloud, there is a necessity to adjust the amount of server resources provided in response to the irregularly increasing and decreasing traffic. This need arises when there is a desire to maintain a constant level of service while, at the same time, minimizing costs as much as possible. There is an abundance of prior research regarding the prediction of future loads from past time-series data. Many of these approaches rely on traditional time-series forecasting, which necessitates that the data used for learning adhere to stationary or unit root processes, or they use deep learning approaches that include using a vast amount of data and parameters. In this study, we propose a traffic forecasting method that includes dynamic window size changes that can follow even slight variation in trends. This method incorporates a fuzzy-entropy-based burst traffic detection in the regression estimation with sliding-window learning. In the evaluation, we conduct four comparative experiments using actual traffic rather than simulations. As a result, compared to the baseline, the proposed reduced the number of request failures and improved the Mean Squared Error between the ideal and the actual container count by 26.4 points on average.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.32(2024) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.32.916
------------------------------},
 title = {Container Auto-scaling System Using Sliding-Window Regression with Fuzzy Entropy},
 volume = {65},
 year = {2024}
}