The predicted load balancing algorithm based on the dynamic exponential smoothing

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 439-447
Author(s):  
Lijie Yan ◽  
Xudong Liu

AbstractTo a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.

Author(s):  
Andrew Q. Philips

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.


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