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

Bai-qi, Miao; Dou, Chao

doi:10.1155/2016/9328062

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…In this work, considering that the user's file size cannot be predicted and the received files will not be the same as our measured results, we need a mechanism to coordinate the interpolation into a linear equation. Based on the promising features studied in the reference works The measurement results of the transfer speed of one file [29][30][31], we therefore choose to use the cubic spline interpolation method to solve this problem.…”

Section: The Mechanism Of File Distributionmentioning

confidence: 99%

“…Interpolations using cubic splines have been well studied in [29][30][31]. In [29], the basis of cubic spline interpolation was introduced.…”

Section: The Mechanism Of File Distributionmentioning

confidence: 99%

“…In [29], the basis of cubic spline interpolation was introduced. Miao et al [30] employed the cubic spline method to predict the storage volume of a data center by interpolating the storage volume time series such that an entire time series with the same number as the former series can be reconstructed. In addition, Mastorakis [31] showed that the cubic spline method is well suited for application to the problem of anomaly detection in cloud environments.…”

Section: The Mechanism Of File Distributionmentioning

confidence: 99%

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On construction of a cloud storage system with heterogeneous software-defined storage technologies

Yang

Hsu

Chan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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In the last decade, cloud computing has attracted more and more attentions in both industry and academia [1-8]. It deeply changed people's lives due to its inherent advantages, such as on-demand self-service, resource pooling and rapid resource elasticity, etc.. With the services provided by cloud computing, users can upload their requirements via the Internet to a cloud environment and receive responses following postprocessing in the cloud environment. Among these services, cloud storage service is one of the important and indispensable services [9-12]. Cloud storage makes data storage a service in which data is outsourced to a cloud server maintained by a cloud provider. With the service, data could be stored remotely into the cloud efficiently and safely. Thus, this service attracts many people, especially enterprises, due to that it brings appealing benefits, e.g., avoidance of capital expenditure on hardware and software, relief of the burden for storage management, etc. [13-15].

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Section: The Mechanism Of File Distributionmentioning

confidence: 99%

“…Interpolations using cubic splines have been well studied in [29][30][31]. In [29], the basis of cubic spline interpolation was introduced.…”

Section: The Mechanism Of File Distributionmentioning

confidence: 99%

Section: The Mechanism Of File Distributionmentioning

confidence: 99%

See 1 more Smart Citation

On construction of a cloud storage system with heterogeneous software-defined storage technologies

Yang

Hsu

Chan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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show abstract

“…Article [45] proposed an irregular sampling estimation method to extract the main trend of a time series, in which the Kalman filter was used to remove the noise, missing data, and outliers; then, cubic spline interpolation and averaging methods were used to reconstruct the main trend. In [46], transport protocols such as optical packet switching and optical burst switching allowed a one-sided view of the traffic flow in the network.…”

Section: An Overview Of Alternative Literature On Big Data Reducing Mmentioning

confidence: 99%

Intelligent Monitoring of Data Center Physical Infrastructure

Matko

Brezovec²,

Milanovič

2019

Applied Sciences

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This paper presents a new intelligent monitoring and event management method for data center physical infrastructure based on multilayer node event processing. The priorities of highly classified data centers are not only the maintenance of the highest level of reliability and availability of the operation, but also fast, specific event identification and energy consumption monitoring, which altogether improve the level of energy efficiency. The new method, using a tree node for each device, improves information about the events in a specific node. Its major advantage is that in the case of a large number of nodes, it considerably reduces the number of identified events (alarms/alerts) that might be the main cause. Fast event processing lowers data center physical infrastructure operating costs, improving at the same time the level of energy efficiency. The experimental results show that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data center physical infrastructure.

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“…It needs to be pointed out that, when using the Kalman filter, an accurate system dynamic model would offer great help to achieve the optimal estimation. Miao et al [ 28 ] used the Kalman filter with several different kinds of system models to remove the noise of the storage volume data of the internet center. Due to the difficulty in obtaining the density characteristic of the practical data, the adaptive model was proposed to capture the characteristics of the moving targets in [ 29 ], and estimate the acceleration based on the adaptive parameter.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 4 publications

References 17 publications

On construction of a cloud storage system with heterogeneous software-defined storage technologies

On construction of a cloud storage system with heterogeneous software-defined storage technologies

Intelligent Monitoring of Data Center Physical Infrastructure

Online Denoising Based on the Second-Order Adaptive Statistics Model

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