Courtney Foots1, Palash Pal2, Rituparna Datta1 and Aviv Segev1, 1University of South Alabama, USA and 2Burdwan University, India
We propose a set of methods to classify vendors based on estimated central processing unit (CPU) performance and predict CPU performance based on hardware components. For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor in the dataset to create classification zones. These zones can be used to list vendors who manufacture hardware that satisfy given performance requirements. We use multi-layered neural networks for performance prediction, which accounts for nonlinearity in performance data. Several neural network architectures are analysed in comparison to linear, quadratic, and cubic regression. Experiments show that neural networks can be used to obtain low prediction error and high correlation between predicted and published performance values, while cubic regression can produce higher correlation than neural networks when more data is used for training than testing. The proposed methods can be used to identify suitable hardware replacements.
Computer Hardware, Performance Prediction and Classification, Neural Networks, Statistical Learning, Regression