F201 TA5: Environment and Society

1 State two ways a data centre can reduce its energy consumption. 2

One mark for each of the following, to a maximum of 2.

• One valid measure
• A second, different valid measure

Model answer. Using free air cooling rather than refrigeration where the climate allows it, and virtualising workloads so that fewer physical servers run, reducing the number sitting idle.

2 Explain why the location of a data centre affects the carbon emissions of the work it does. 4

One mark for each of the following, to a maximum of 4.

• Emissions depend on how the local electricity is generated
• The same workload can emit several times as much on a dirtier grid
• Climate affects the cooling energy required
• A consequence, such as carbon aware placement or scheduling

Model answer. The emissions from a computing workload come almost entirely from the electricity it consumes, and the carbon intensity of electricity varies enormously by grid: a kilowatt hour on a coal-dominated grid can carry several times the emissions of the same kilowatt hour on a grid dominated by wind, hydro or nuclear. The identical job therefore has a very different footprint depending only on where it runs. Location also changes how much electricity is needed in the first place, because a facility in a cool climate can use outside air for much of the year while one in a hot climate runs refrigeration continuously, and cooling has historically been a large share of total consumption. The practical consequence is that placement and timing become environmental decisions: flexible batch work and model training can be scheduled into regions and hours when the grid is cleanest, without changing the work itself.

3 Discuss the environmental impact of big data and machine learning, and evaluate what organisations can realistically do to reduce it. 8

One mark for each of the following, to a maximum of 8.

• Energy use of servers and cooling is addressed
• A measure such as PUE is named and used
• Storage's continuous cost is addressed
• Training and inference are distinguished
• Water, materials or e-waste are addressed
• Realistic mitigations are proposed
• The rebound effect or a limitation is acknowledged
• A clear evaluative judgement

Model answer. The impact is larger and broader than the electricity bill suggests. Servers draw power continuously and an idle machine still consumes a substantial share of its peak, while cooling adds an overhead measured by power usage effectiveness: a facility at a PUE of 1.8 spends eighty per cent as much again on cooling and losses as it does on computing, where the best modern designs run near 1.1. Storage is the cost organisations most often overlook, because data kept for years draws power every second whether or not a single query ever reads it, which makes a neglected data lake a continuing environmental liability as well as a compliance one. In machine learning the headline figure is training, which can occupy thousands of accelerators for weeks and is repeated at every retrain, but for any widely used model the cumulative energy of inference across billions of individual queries exceeds it. Beyond energy there is water, since evaporative cooling consumes fresh water in regions that may be water stressed, and materials, since a three to five year hardware cycle depends on mined metals and produces a continuous stream of e-waste of which only a minority is formally recycled. What organisations can realistically do divides into the effective and the cosmetic. The genuinely effective measures are consolidating workloads through virtualisation so fewer machines idle, using elastic capacity that is released when a job finishes rather than running overnight, scheduling flexible batch work and training into regions and hours when the grid is cleanest, enforcing retention policies so unused data is deleted and cold data moved to lower-power storage, writing queries that scan partitions and columns rather than whole tables, and reusing or fine-tuning a pretrained model instead of training from scratch. Procuring renewable supply matters, though matching it hour by hour is far stronger than an annual accounting claim, and extending hardware life with repair and certified recycling addresses the materials side that energy measures ignore. The limitation has to be stated honestly: efficiency alone has not reduced total consumption, because data centre efficiency has improved markedly while total data centre energy use has continued to rise, as cheaper computing simply gets used more. Judged overall, the measures with the largest certain effect are the unglamorous ones, deleting data that serves no purpose, not running what does not need to run, and sizing models and queries to the question, because they reduce the work itself rather than making the same growing workload marginally cheaper to perform.