URegM: a unified prediction model of resource consumption for
  refactoring software smells in open source cloud
Asif Imran Tevfik Kosar
The low cost and rapid provisioning capabilities have made the cloud a
desirable platform to launch complex scientific applications. However, resource
utilization optimization is a significant challenge for cloud service
providers, since the earlier focus is provided on optimizing resources for the
applications that run on the cloud, with a low emphasis being provided on
optimizing resource utilization of the cloud computing internal processes. Code
refactoring has been associated with improving the maintenance and
understanding of software code. However, analyzing the impact of the
refactoring source code of the cloud and studying its impact on cloud resource
usage require further analysis. In this paper, we propose a framework called
Unified Regression Modelling (URegM) which predicts the impact of code smell
refactoring on cloud resource usage. We test our experiments in a real-life
cloud environment using a complex scientific application as a workload. Results
show that URegM is capable of accurately predicting resource consumption due to
code smell refactoring. This will permit cloud service providers with advanced
knowledge about the impact of refactoring code smells on resource consumption,
thus allowing them to plan their resource provisioning and code refactoring
more effectively.