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.

翻译：云计算的低成本与快速部署能力使其成为运行复杂科学应用的理想平台。然而，资源利用优化对云服务提供商而言仍是一项重大挑战，因为此前的研究主要聚焦于优化云上运行的应用程序资源，而对云计算内部进程资源利用优化的关注度较低。代码重构虽能提升软件代码的可维护性与可理解性，但云平台源代码重构的影响及其对云资源使用的作用机制仍需深入分析。本文提出一种名为统一回归建模（URegM）的框架，用于预测代码异味重构对云资源使用的影响。我们以复杂科学应用作为工作负载，在真实云环境中开展实验验证。结果表明，URegM能够准确预测代码异味重构导致的资源消耗变化。这使云服务提供商能够预先掌握代码异味重构对资源消耗的影响，从而更有效地规划资源供给与代码重构策略。