Estimating Energy Impact of Software Releases and Deployment Strategies: The KPMG Case Study

摘要

Background. Often motivated by optimization objectives, software products are characterized by different subsequent releases and deployed through different strategies. The impact of these two aspects of software on energy consumption has still to be completely understood and can be improved by carrying out ad-hoc analyses for specific software products. Aims. In this research we report on an industrial collaboration aiming at assessing the different impact that releases and deployment strategies of a software product can have on the energy consumption of its underlying hardware infrastructure. Method. We designed and performed an empirical experiment in a controlled environment. Deployment strategies, releases and use case scenarios of an industrial third-party software product were adopted as experimental factors. The use case scenarios were used as a blocking factor and adopted to dynamically load-test the software product. Power consumption and execution time were selected as response variables to measure the energy consumption. Results. We observed that both deployment strategies and software releases significantly influence the energy consumption of the hardware infrastructure. A strong interaction between the two factors was identified. The impact of such interaction highly varied depending on which use case scenario was considered, making the identification of the most frequently adopted use case scenario critical for energy optimisation. The collaboration between industry and academia has been productive for both parties, even if some practitioners manifested low interest/awareness on software energy efficiency. Conclusions. For the software product considered there is no absolute preferable release or deployment strategy with respect to energy efficiency, as the interaction of these factors has to be considered. The number of machines involved in a software deployment strategy does not simply constitute an additive effect of the energy consumption of the underlying hardware infrastructure.