Towards an Energy-Consumption Based Complexity Classification for Resource Substitution Strategies

Protecting the environment by saving energy and thus reducing carbon dioxide is one of today’s hottest topics and is of a rapidly growing importance in the computing domain. In addition, to ecological reasons here issues such as the up-time of mobile or embedded devices, battery charge cycles etc. are key problems. Recent studies have shown that software has a major impact onto the energy consumption of the device it is executed on. Thus, intelligent selection and assembly of software components promises significant savings. However, this requires knowledge about how much energy a (software) component consumes. In other words, a classification scheme following the idea of the European Union energy label is required. This paper discusses recent findings and first ideas of establishing an energy classification scheme for software, using the ’bigO’ notation as its general metaphor. The scheme is motivated, introduced and validated by using resource substitution strategies, as one means for optimizing energy consumption via software adaptation. We demonstrate that the classification scheme can be used to characterize the fitness of a strategy and/or algorithm. Furthermore, we discuss to use such energy labels/classes when estimating the energy consumption of systems assembled from different components.