Yuebin Zhou
School of Mechanical and Automotive Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China
Guixiong Liu
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China
Haibing Zhu
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China
ABSTRACT
In order to solve the networked intelligent sensor system load balance problem and improve service response speed, a load balance realization method based on Probabilistic Preferred Grey Markov Chain Prediction (PP-GMCP) algorithm is proposed. This method real-time monitors Network Capable Application Processor (NCAP) load status, combines residual correction and grey Markov chain prediction, effectively predicts NCAP load capacity. A load balance simulation platform based on OPNET is constructed to validate algorithm performance. The test shows that the PP-GMCP algorithm effectively improves the service request processing speed, compared to weighted round robin and least connection scheduling its average service response delay reduces 11.1 and 25.1%, respectively, the NCAP load fluctuation range is the smallest and obtains better load balance effect.
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How to cite this article
Yuebin Zhou, Guixiong Liu and Haibing Zhu, 2013. Networked Intelligent Sensor System Load Balance Based on Pp-gmcp Algorithm. Journal of Applied Sciences, 13: 1551-1557.
DOI: 10.3923/jas.2013.1551.1557
URL: https://scialert.net/abstract/?doi=jas.2013.1551.1557
DOI: 10.3923/jas.2013.1551.1557
URL: https://scialert.net/abstract/?doi=jas.2013.1551.1557
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