Main Trend Extraction of the Storage Volume for Internet Data Center

Bai-qi, Miao; Dou, Chao; Jin, Xuebo

doi:10.1109/is3c.2016.250

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Then, an average method is used to achieve the main trend. Part of the developed method has been mentioned in the conference paper [20] with 4 pages. By comparison, this manuscript gives the details of the main trend extraction method and discusses the experiments more comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center

Bai-qi

Dou²

2016

Computational Intelligence and Neuroscience

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The storage volume of internet data center is one of the classical time series. It is very valuable to predict the storage volume of a data center for the business value. However, the storage volume series from a data center is always “dirty,” which contains the noise, missing data, and outliers, so it is necessary to extract the main trend of storage volume series for the future prediction processing. In this paper, we propose an irregular sampling estimation method to extract the main trend of the time series, in which the Kalman filter is used to remove the “dirty” data; then the cubic spline interpolation and average method are used to reconstruct the main trend. The developed method is applied in the storage volume series of internet data center. The experiment results show that the developed method can estimate the main trend of storage volume series accurately and make great contribution to predict the future volume value.

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Section: Introductionmentioning

confidence: 99%

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center

Bai-qi

Dou²

2016

Computational Intelligence and Neuroscience

Self Cite

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Performance monitoring and parameter optimization of fuzzy controller in combustion process of coke Oven

Zhu

Lai

2017

2017 11th Asian Control Conference (ASCC)

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Online Denoising Based on the Second-Order Adaptive Statistics Model

Tang

et al. 2017

Sensors

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Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule–Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

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Main Trend Extraction of the Storage Volume for Internet Data Center

Cited by 3 publications

References 9 publications

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center

Main Trend Extraction Based on Irregular Sampling Estimation and Its Application in Storage Volume of Internet Data Center

Performance monitoring and parameter optimization of fuzzy controller in combustion process of coke Oven

Online Denoising Based on the Second-Order Adaptive Statistics Model

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