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Research Article

Networked Intelligent Sensor System Load Balance Based on Pp-gmcp Algorithm

Yuebin Zhou, Guixiong Liu and Haibing Zhu
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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


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