Option B, because the hydro region has the lowest absolute intensity

Option A, because the solar region’s large intensity variation creates a meaningful clean window, the job is flexible with deadline slack, and no data transfer is required

All three options are equally feasible because they all target carbon reduction

Option C, because the matching algorithm consumes the most energy and the hydro region has the lowest intensity

Inventory reconciliation, because its 2-hour SLA and 1-hour job duration leave only 1 hour of scheduling slack

Sales analytics, because it runs overnight when carbon intensity is already low

Recommendation retraining, because machine learning jobs are too computationally expensive to defer

Order processing, because it is an interactive foreground workload that cannot be deferred at all

Temporal shifting within Region B after first transferring all videos there permanently

Temporal shifting within Region A, because avoiding data transfer overhead is always preferable

Neither strategy, because video encoding is an inflexible workload that cannot be shifted

Spatial shifting to Region B, because Region A’s flat intensity profile means temporal shifting offers almost no benefit, while Region B has substantially lower intensity

Organisation A, because the fixed engineering cost is spread across a much larger volume of flexible compute, producing substantially more total carbon savings per unit of investment

Both organisations equally, because the engineering effort is the same

Organisation B, because smaller organisations have a greater responsibility to reduce emissions

Neither organisation, because carbon-aware scheduling only works for hyperscale cloud providers

Start the job by 22:00 the previous evening regardless of carbon intensity, because waiting for the 01:00 clean window would miss the 08:00 deadline

Cancel the job because no clean window can accommodate 10 hours

Split the job into two 5-hour segments and run each in a separate clean window

Extend the deadline to 12:00 to allow the job to run entirely within a clean window

The health records system is inflexible; the MRI analysis has limited shifting potential (3 hours 15 minutes of slack); the research pipeline has high shifting potential (days of slack)

The health records and MRI analysis are both inflexible; only the research pipeline can be shifted

All three workloads are inflexible because they operate in a healthcare context where delays are unacceptable

All three workloads are flexible because they are batch processes running on cloud infrastructure

The overnight window is always preferable because cost savings can be used to purchase carbon offsets

Carbon and cost optimisation point in opposite directions in this grid; the scheduling system must accept higher cost for lower carbon, higher carbon for lower cost, or find a compromise window

This conflict does not arise in practice because low-carbon periods always coincide with low-cost periods

The organisation should always choose the midday window because carbon savings should override cost considerations

The proactive scheduler runs jobs faster because it reserves more compute resources in advance

The proactive scheduler can see that a clean window is approaching and hold jobs for it, whereas the reactive scheduler can only see the current intensity and may start jobs during a dirty period or defer them without a plan

The proactive scheduler uses more accurate carbon intensity data

Cloud providers do not allow workloads to be moved between regions

Oregon does not have sufficient datacenter capacity to process the job

Hydroelectric power is unreliable and may not be available when the job runs

The energy and time cost of transferring the large dataset across regions may offset the carbon benefit of running in a cleaner region

Coal plants cannot adjust their output in response to changes in computing demand

The carbon intensity variation across the day is too small to create a meaningful difference between the best and worst execution windows

Coal-dominated grids do not support batch processing workloads

Temporal shifting requires wind or solar generation to function