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Title: Modelling and assessing the environmental impacts of software
Author: Williams, Daniel R.
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2013
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Software is used on billions of devices every day for numerous activities and services that the modern world relies upon. It also has the potential to control energy consumption and maximise device efficiency, thus minimising device and service environmental impact. The academic background to the environmental impact of software, at the beginning of this research, was minor and little utilised by information and communications technology (ICT) organisations. Consequently, this research analysed and modelled three main software types enabling the environmental analysis of software use to take place with real data and information. Firstly, Operating Systems (OS) and their power management features were analysed. as form the foundation of any software based activity and control device componentry, and thus overall device energy consumption. The overall impact of an as, not just a devices idle or maximum power, was investigated with a new set of measurements, methods, and models constructed in this research. Secondly, new methodologies to measure the energy consumption of electronic software distribution (ESD) and cloud computing were created, which spanned the entire life cycle of the data route. Both technologies were modelled and measured across a number of real scenarios. ESD and cloud computing can be utilised to dematerialise and increase the efficiency of some high energy impact activities. Additionally, it was found that cloud computing can be utilised to reduce the impact of data and process intensive computing, but can also be wasteful compared to traditional computing when utilised for certain types of low resource software activities. Finally, a novel framework was created to enable the analysis and comparison of the energy consumption and greenhouse gas (GHG) emissions of the same software activity across a range of devices and as, according to the technical functionality and price of a set of devices. This framework enables the analysis of devices according to their software GHG emission efficiencies across different platforms and may allow manufacturers and consumers to maximise and drive forward environmental awareness. In conclusion, this research has set the foundations for quantifying, and has demonstrated, the large potential that software has to reduce energy consumption and overall environmental impact.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available