Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabi...Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is attained.In addition,every workload or application required for the framework is characteristic and these essentials change over time.But,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CPU,RAM,and network bandwidth utilization)based on the usage history in each cloud server.Then,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.展开更多
The presence of dispersion/variability in any process is understood and its careful monitoring may furnish the performance of any process. The interquartile range (IQR) is one of the dispersion measures based on lower...The presence of dispersion/variability in any process is understood and its careful monitoring may furnish the performance of any process. The interquartile range (IQR) is one of the dispersion measures based on lower and upper quartiles. For efficient monitoring of process dispersion, we have proposed auxiliary information based Shewhart-type IQR control charts (namely IQRr and IQRp charts) based on ratio and product estimators of lower and upper quartiles under bivariate normally distributed process. We have developed the control structures of proposed charts and compared their performances with the usual IQR chart in terms of detection ability of shift in process dispersion. For the said purpose power curves are constructed to demonstrate the performance of the three IQR charts under discussion in this article. We have also provided an illustrative example to justify theory and finally closed with concluding remarks.展开更多
The characteristics of density yield curve of coal and distribution curve of products can be described with median, quartile deviation, the quartile measure of skewness and kurtosis like K. On the basis of 16 groups o...The characteristics of density yield curve of coal and distribution curve of products can be described with median, quartile deviation, the quartile measure of skewness and kurtosis like K. On the basis of 16 groups of coal density composition data and their jigging stratification data derived from the pilot jig, the regression analysis has been done for the relationship between the characteristic values of the density curve and the characteristic values of the distribution curve.The results show as follow: (1) The bigger the skewness of the density curve, the bigger the probable error (Ep) and imperfection (I ) are. (2) The bigger the median of density curve, the smaller the probable error or imperfection is. (3) The characteristic values of density curve have no influence on the kurtosis K of the distribution curve.展开更多
The procedure of stratified double quartile ranked set sampling （SDQRSS） method is introduced to estimate the population mean. The SDQRSS is compared with the simple random sampling （SRS）, stratified ranked set sa...The procedure of stratified double quartile ranked set sampling （SDQRSS） method is introduced to estimate the population mean. The SDQRSS is compared with the simple random sampling （SRS）, stratified ranked set sampling （SRSS） and stratified simple random sampling （SSRS）. It is shown that SDQRSS estimator is an unbiased of the population mean and more efficient than SRS, SRSS and SSRS for symmetric and asymmetric distributions. In addition, by SDQRSS we can increase the efficiency of mean estimator for specific value of the sample size.展开更多
文摘Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is attained.In addition,every workload or application required for the framework is characteristic and these essentials change over time.But,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CPU,RAM,and network bandwidth utilization)based on the usage history in each cloud server.Then,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.
文摘The presence of dispersion/variability in any process is understood and its careful monitoring may furnish the performance of any process. The interquartile range (IQR) is one of the dispersion measures based on lower and upper quartiles. For efficient monitoring of process dispersion, we have proposed auxiliary information based Shewhart-type IQR control charts (namely IQRr and IQRp charts) based on ratio and product estimators of lower and upper quartiles under bivariate normally distributed process. We have developed the control structures of proposed charts and compared their performances with the usual IQR chart in terms of detection ability of shift in process dispersion. For the said purpose power curves are constructed to demonstrate the performance of the three IQR charts under discussion in this article. We have also provided an illustrative example to justify theory and finally closed with concluding remarks.
文摘The characteristics of density yield curve of coal and distribution curve of products can be described with median, quartile deviation, the quartile measure of skewness and kurtosis like K. On the basis of 16 groups of coal density composition data and their jigging stratification data derived from the pilot jig, the regression analysis has been done for the relationship between the characteristic values of the density curve and the characteristic values of the distribution curve.The results show as follow: (1) The bigger the skewness of the density curve, the bigger the probable error (Ep) and imperfection (I ) are. (2) The bigger the median of density curve, the smaller the probable error or imperfection is. (3) The characteristic values of density curve have no influence on the kurtosis K of the distribution curve.
文摘The procedure of stratified double quartile ranked set sampling （SDQRSS） method is introduced to estimate the population mean. The SDQRSS is compared with the simple random sampling （SRS）, stratified ranked set sampling （SRSS） and stratified simple random sampling （SSRS）. It is shown that SDQRSS estimator is an unbiased of the population mean and more efficient than SRS, SRSS and SSRS for symmetric and asymmetric distributions. In addition, by SDQRSS we can increase the efficiency of mean estimator for specific value of the sample size.
