Performance prediction model for cloud service selection from smart data

Cloud computing is a computing model that has experienced significant growth in the world in contemporary time. Cloud providers offer services to consumers at different levels of performance, costs, and configurations. Many enterprises and organizations are planning to move their services to a cloud platform. The most challenging issue for them is choosing the most appropriate services that meet their requirements. In this paper, we try to tackle this challenge by automating the selection process based on actual workload pattern from Smart data and resource demand acquired from existing service history data. An automatic performance prediction model based on Naïve Bayes classifiers is proposed to predict the performance metrics of cloud nodes with respect to different options for configuration of their resources. We examined Naïve Bayes classifier along with kernel density estimation to solve the zero variance of feature distribution and enhance the accuracy of predictions. We also evaluated our model using a detailed one-year dataset from a realistic environment with thousands of records and hundreds of machines. A simulation on the MATLAB was performed and the results showed that the proposed model indicates how naïve Bayes can provide accurate and efficient results.