The traditional method of measuring the energy consumption and CO2 emissions of ICT equipment for carbon reduction purposes may not necessarily be the best, according to researchers from University of Melbourne’s partner Centre for Energy Efficient Telecommunications, and Bell Labs.
In an effort to help organisations become more energy efficient, the researchers released a study that tested various models for measuring energy consumption and CO2 emissions of ICT equipment.
The ICT industry is guilty of producing more than 830 million tonnes of carbon dioxide (CO2) annually, which is about 2 per cent of global CO2 emissions, and is expected to double by 2020, according to the researchers.
They compared four models: ‘top down’, ‘bottom up’, ‘coarse grained’ and ‘fine grained’. The traditional top-down model measures energy consumption and service traffic from an entire network of services. The bottom-up model is more accurate because it measures individual equipment units.
The coarse-grained and fine-grained models measure the equipment class and can be used where there is a great complexity of services and where the bottom-up model might not be feasible.
Using a simulated network and a deployed network, the California Research and Education Network, the researchers tested the models and found the top-down model to be the least accurate in estimating energy consumption and service traffic compared to the others.
“The fine-grained model has significantly greater accuracy under conditions in which the network service traffic is concentrated in a small number of nodes,” the report reads.
“The coarse-grained model, provided that the distribution of the service traffic weighting at each node is not highly skewed… can exhibit an accuracy similar to that of the fine-grained model and may be preferable because of its reduced complexity in obtaining network measurements.
“The top-down model is accurate only under the network conditions in which the network service traffic weighting is similar among all the nodes in the network because it has a large MEE [Model Estimation Error] in other cases."
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