Machine Learning for the Prediction of App Energy Consumption from Appstore Data

• Autor(a) principal: Valente, Daniel Afonso
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.6/13225
• topic: Aprendizagem Automática; Aptoide; Computação Verde; Consumo Energético; Energia; Móvel; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

The mobile market has seen tremendous development throughout the past few years both in terms of hardware and the software that is available for the devices. Despite this, the batteries that power these devices have not seen major improvements and have been unable to accompany the progress seen in this field. Due to this phenomenon, researchers have been showing a growing interest in the development of green computing solutions in order to spend the least amount of energy possible when using mobile devices. This as presented itself in a plethora of ways, from the accurate evaluation of the energy consumption of applications through the use of energy models and profilers to the assessment and development of better coding practices with energy conservation as the main focus. However, there have been few to no studies regarding the development of user-side solutions to help solve this problem. In order to fill this gap in research this study focuses on providing a machine learning solution with the intent of identifying links between the information available in the store page of an application and its energy consumption to develop an a priori method for the classification and certification of mobile applications. Hence the main contribution of this project resides on the previously mentioned machine learning model, adapted to the Aptoide appstore and mainly targeting applications that belong to the games category, given that these have the highest volume of downloads and interest by the users of the appstore.