Virtual Capacity Scheduling for Carbon-Aware Compute Load Shaping
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Solution Overview
Problem
Computing systems generate carbon emissions and peak power demand, leading to increased costs and environmental impact, which existing technologies have not adequately addressed.
Innovation Solution
A system that shapes compute load by determining virtual capacity based on load forecasts, power models, and carbon intensity forecasts, allowing real-time scheduling of jobs across multiple cells to reduce carbon emissions and peak power demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute load is increased to maximize computational capacity utilization, then productivity is improved, but carbon emissions and power consumption increase
Solution Approach 1:
The system dynamically adjusts virtual capacity allocations across cells based on real-time carbon intensity forecasts and current load conditions. Instead of static capacity planning, the virtual capacity engine continuously reconfigures capacity distributions to shift compute loads from high-carbon to low-carbon time periods and locations, maintaining productivity while reducing emissions
Solution Approach 2:
The system changes the parameter of capacity allocation from fixed physical capacity to dynamic virtual capacity. By modifying capacity parameters in response to carbon intensity variations, the system can redirect compute jobs to optimal time-location combinations without compromising overall computational output
2Object-generated harmful factors
If compute load is shifted to nighttime to reduce carbon emissions, then carbon emissions are reduced, but job execution time increases
Solution Approach 1:
The system performs preliminary actions by pre-forecasting carbon intensity patterns and proactively scheduling flexible compute jobs during low-carbon periods. The virtual capacity engine anticipates future carbon conditions and adjusts capacity allocations in advance, allowing jobs to be executed during optimal low-emission windows without last-minute delays
Solution Approach 2:
The virtual capacity acts as an intermediary layer between physical compute resources and job scheduling decisions. This intermediary enables fine-grained control over when and where compute work is executed, facilitating smooth load shifting to low-carbon periods while maintaining service level agreements through capacity buffering
3Use of energy by stationary object
If virtual capacity is reduced to lower power consumption, then power usage is reduced, but computational capacity is limited
Solution Approach 1:
The system applies partial action by allocating virtual capacity only when and where it is needed based on carbon intensity conditions. Instead of maintaining full physical capacity continuously, the system activates computational resources partially during low-carbon periods and scales back during high-carbon periods, achieving energy reduction without permanently limiting capacity
Solution Approach 2:
The virtual capacity mechanism serves multiple functions simultaneously: it acts as a capacity allocation tool, an energy management instrument, and a carbon reduction strategy. This multi-functionality allows the same virtual capacity layer to optimize for both productivity and power consumption depending on prevailing conditions
4Object-generated harmful factors
If real-time scheduling is implemented to optimize carbon emissions, then carbon emissions are reduced, but system complexity increases
Solution Approach 1:
The system segments the scheduling problem into manageable components: carbon intensity forecasting, virtual capacity allocation, and job scheduling decisions. By dividing the complex real-time optimization into separate modular functions, the system reduces overall complexity while achieving carbon emission reduction through coordinated operation of these segments
Data Source
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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.