PREDICTION OF WORKLOAD PERFORMANCE OF DATA CENTER USING MACHINE LEARNING TECHNIQUES

PREDICTION OF WORKLOAD PERFORMANCE OF DATA CENTER USING MACHINE LEARNING TECHNIQUES
Author(s):
Vidya Rajasekaran, Arul Natarajan, A.Sai Preetham, K.Ramakrishna, Amjan Sheik, J.Ramesh Babu
Keywords:
Machine Learning, Quadratic Discriminator Analysis, Ensemble Method
Abstract
The workload performance in a data center depends on the available resources and the workload. If the workload is too low whereas there are many computational and network resources, then those resources are not utilized to their maximum capacity because of low workload. Likewise, a high workload with low resources is not also advisable as the resources will not be able to meet up the demand. This project aims at predicting the performance by analyzing a data set, consisting of the above mentioned properties by using the Random Forest Classifier, Gradient Booster Algorithm, Logistic Regression, ANN (Artificial Neural Networks) of Sklearn’s Ensemble Module and the results of these algorithms are further tallied by executing Quadratic Discriminator Analysis on the same dataset.
Article Details
Unique Paper ID: 160902 Publication Volume & Issue: Volume 10, Issue 2 Page(s): 15 - 19
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