Towards Sustainable Cloud Software Systems through Energy-Aware Code Smell Refactoring

Software applications and workloads, especially within the domains of Cloud computing and large-scale AI model training, exert considerable demand on computing resources, thus contributing significantly to the overall energy footprint of the IT industry. In this paper, we present an in-depth analysis of certain software coding practices that can play a substantial role in increasing the application's overall energy consumption, primarily stemming from the suboptimal utilization of computing resources. Our study encompasses a thorough investigation of 16 distinct code smells and other coding malpractices across 31 real-world open-source applications written in Java and Python. Through our research, we provide compelling evidence that vari-ous common refactoring techniques, typically employed to rectify specific code smells, can unintentionally escalate the application's energy consumption. We illustrate that a discerning and strategic approach to code smell refactoring can yield substantial energy savings. For selective refactorings, this yields a reduction of up to 13.1 % of energy consumption and 5.1 % of carbon emissions per workload on average. These findings underscore the potential of selective and intelligent refactoring to substantially increase energy efficiency of Cloud software systems.