Comparative Analysis of Neural Network Techniques Vs Statistical Methods in Capacity Planning

Capacity planning is a technique which can be used to predict the computing resource needs of an organization for the future after studying current usage patterns. This is of special import for adaptive enterprises, given the large infrastructure and large number of users. Determining resource needs beforehand can be very beneficial because it is a proactive approach and helps prevent resource crunches and service level violations. Accuracy of the predicted values, however, depends upon the methods used for the forecast and also upon the accuracy of the historical data. Historical data in the capacity planning sense is system performance data. Most of the approaches used for such a prediction make use of statistical methods or are based on queuing theory. This paper compares the traditional statistical based methods with a method based on neural networks. The training set for the neural network consists of historical values of a metric (for example CPU utilization percentage) for which the prediction is to be done. The advantages of this method over other methods have also been discussed. From the predicted information, we illustrate how capacity planning is done.