Real-Time Server Overloaded Monitoring Algorithm Using Back Propagation Artificial Neural Network
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© 2017 IEEE. In recent years, downtime and information loss problems of server computers have become more critical. Even if a server has antivirus and CPU overload checking programs, it may occasionally be broken or slowed down. Hypothetically, there are possible indications of the problems under specific external situations such as high temperature, low fan speed, and extreme main board vibration. In order to recognize the correlation between external conditions and the overloaded problems, a monitoring computer collects data from external sensors. By using an accelerometer, an anemometer, and a temperature sensor, the monitoring algorithm is able to predict the target computer's status. A web application has been developed to help server managers to remotely know how server computers are operating. This paper proposes a monitoring algorithm to diagnose server overload for a target computer, based on the Fast Fourier Transform, multivariate linear regression, and learning algorithms. As a result, this paper suggests that a monitoring algorithm can be implemented with an artificial neural network that warns of possible malfunction cases.
author list (cited authors)
Choi, J., Shen, C., Hannemann, J., & Bhattacharyya, S.