Research progress on a modular tool for better leveraging energy impacts and savings of and by ICT service

In 2008, Orange Labs has contributed to the ITU-T Focus Group "ICT and Climate Change", in particular with a methodology to assess thoroughly the energy consumption by ICTs services and to determine the technical quantities on which they depend. [1] In IEEE/Telescon 2009 [2], Orange Labs presented its first element of research, which gives more leverage to reduce the energy impact of ICT service and to increase saving by ICT in other sectors through dematerialization or substitution. The main interest of the method was to be more precise, to focus on service in order to complement the ISO 14040 Life Cycle Assessment method (LCA) and the ETSI and ITU-T standards adapted to ICT, and bring them a more systematic method reducing the common risk of huge errors on data and allocation. Progresses of the research work have been published in Genoa ETSI Workshop on Energy Efficiency [3], and ITU-T / IEEE kaleidoscope 2013 [4]. This last contribution showed a reduction of manufacturing impact, which implies that further studies on the service must focus more on the energy consumption in use phase giving more leverage possibility to operators or ICT service users. The developed method is based on a recognized method in energetic sector [5] and should be more reusable by its modularity. It consists in splitting the service in modules characterized by their "local rate" of consumption which is the consumption per unit of characteristic flow of the module, and in linking them to the service with their "weight" which contains the parameters of the consumption. A sensitivity analysis using a simulation tool developed on Scilab allows quantifying the influence of the main parameters. This paper presents the latest progress of the method with the solved issues from previous applications on a simple service. Some of the modules used in the energy impact model (consumption rate per unit of module operation and per unit of one global service) and the identified issues seen in the manufacturing phase are presented. The leveraging power of the method will also be shown through the tool result for optimizing the energy key performance indicators.