Computing resource prediction for MapReduce applications using decision tree
The cloud computing paradigm offer users access to computing resource in a pay-as-you-go manner. However, to both cloud computing vendors and users, it is a challenge to predict how much resource is needed to run an application in a cloud at a required level of quality. This research focuses on developing a model to predict the computing resource consumption of MapReduce applications in the cloud computing environment. Based on the Classified and Regression Tree (CART), the proposed approach derives knowledge of the relationship among the application features, quality of service, and amount of computing resource, from a small training. The experiments show that the prediction accuracy is as high as 80%. This research can potentially benefit both the cloud vendors and users through improving resource management and reducing costs.