Verifying Execution Prediction Model based on Learning Algorithm for Real-time Monitoring

KIPS Journal A (2001 ~ 2012) , Vol. 11A, No.4, pp.243-250, April 2004
10.3745/KIPSTA.2004.11A.4.243, Full Text

Abstract

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitationthat the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.


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Cite this paper

[KIPS Transactions Style]
Y. S. Jeong, T. W. Kim, and C. H. Chang, "Verifying Execution Prediction Model based on Learning Algorithm for Real-time Monitoring," KIPS Journal A (2001 ~ 2012) , Vol.11A, No.4, pp.243-250, 2004, DOI: 10.3745/KIPSTA.2004.11A.4.243.

[IEEE Style]
Yoon Seok Jeong, Tae Wan Kim, and Chun Hyon Chang, "Verifying Execution Prediction Model based on Learning Algorithm for Real-time Monitoring," KIPS Journal A (2001 ~ 2012) , vol. 11A, no. 4, pp. 243-250, 2004. DOI: 10.3745/KIPSTA.2004.11A.4.243.

[ACM Style]
Jeong, Y. S., Kim, T. W., and Chang, C. H. 2004. Verifying Execution Prediction Model based on Learning Algorithm for Real-time Monitoring. KIPS Journal A (2001 ~ 2012) , 11A, 4, (2004), 243-250. DOI: 10.3745/KIPSTA.2004.11A.4.243.