Virtual Capacity Scheduling for Carbon-Aware Compute Cell Load
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Solution Overview
Problem
Computing systems generate significant carbon emissions and peak power demand, leading to increased costs and environmental impact, particularly due to variable power sources and time-dependent electricity pricing.
Innovation Solution
A system that determines a virtual capacity for computing cells based on load forecasts, power usage models, and carbon intensity forecasts, allowing for real-time adjustment of compute load execution to reduce carbon emissions and peak demand by shifting non-time-sensitive jobs to low-carbon hours and optimizing resource allocation across geographically diverse data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute load is executed at high capacity utilization, then productivity is improved, but carbon emissions and power consumption increase
Solution Approach 1:
The system dynamically adjusts the virtual capacity of computing cells based on real-time carbon intensity forecasts and current load conditions. Instead of static capacity allocation, the virtual capacity evolves continuously, allowing the system to maintain high productivity during low-carbon periods while reducing emissions during high-carbon periods through adaptive capacity modulation.
Solution Approach 2:
The invention changes the operational parameters of computing cells by introducing virtual capacity as a time-varying parameter that reflects carbon intensity conditions. This parameter adjustment allows the system to optimize the trade-off between productivity and carbon emissions by modifying effective capacity utilization based on external environmental factors rather than maintaining fixed operational parameters.
2Object-generated harmful factors
If compute load is shifted to nighttime hours, then carbon emissions are reduced, but productivity during daytime hours decreases
Solution Approach 1:
The system performs preliminary actions by pre-scheduling non-time-sensitive compute jobs during nighttime low-carbon hours before their actual execution is needed during daytime. This advance planning allows the system to shift carbon-intensive work to off-peak hours while maintaining daytime productivity through pre-computed results or cached outputs.
Solution Approach 2:
The invention implements periodic action by rhythmically alternating between high-utilization periods (when carbon intensity is low) and reduced-utilization periods (when carbon intensity is high). This periodic modulation of compute load follows the temporal pattern of carbon intensity variations, creating a sustainable operational rhythm that balances productivity with environmental impact.
3Object-generated harmful factors
If virtual capacity is reduced to lower carbon footprint, then carbon emissions decrease, but available computational capacity is limited
Solution Approach 1:
The system adds a temporal dimension to capacity management by introducing time-varying virtual capacity that extends beyond traditional spatial resource allocation. This fourth dimension of capacity management allows the system to optimize carbon footprint without permanently reducing computational capacity, as capacity can be fully utilized during low-carbon periods while appearing reduced during high-carbon periods.
Solution Approach 2:
Virtual capacity acts as an intermediary layer between physical hardware capacity and actual compute job execution. This intermediary abstraction allows the system to decouple physical resource availability from effective capacity utilization, enabling flexible adjustment of computational capacity based on carbon intensity conditions while maintaining full hardware availability when needed.
4Object-generated harmful factors
If real-time scheduling is implemented to optimize carbon emissions, then carbon footprint is reduced, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring carbon intensity forecasts, current cell load conditions, and virtual capacity utilization. This real-time feedback loop enables the scheduling system to dynamically adjust job allocation decisions based on changing environmental conditions and system state, optimizing carbon emissions through responsive adaptive control rather than static predetermined schedules.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for shaping compute load using virtual capacity. In one aspect, a method includes obtaining a load forecast that indicates forecasted future compute load for a cell, obtaining a power model that models a relationship between power usage and computational usage for the cell, obtaining a carbon intensity forecast that indicates a forecast of carbon intensity for a geographic area where the cell is located, determining a virtual capacity for the cell based on the load forecast, the power model, and the carbon intensity forecast, and providing the virtual capacity for the cell to the cell.