Data Center Workload Management for Heat Reclamation
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Solution Overview
Problem
Existing data center energy reclamation methods are inefficient as they do not maximize energy capture and consider factors like workload distribution, thermal design power, and heat flux, leading to untapped opportunities for heat reuse, such as electrical generation.
Innovation Solution
Implementing adaptive and dynamic workload management systems that use algorithms, including AI, to concentrate heat generation at specific racks by optimizing workload distribution and coolant temperature management, thereby increasing the amount of reclaimable heat.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If heat is captured and reused for heating buildings, then energy reclamation is achieved, but energy capture efficiency is not maximized
Solution Approach 1:
The system dynamically adjusts workload distribution parameters and coolant temperature parameters to optimize heat flux concentration at specific racks, transforming the static energy reclamation approach into a dynamic optimization system that maximizes thermoelectric generator efficiency
Solution Approach 2:
The invention implements adaptive workload management that continuously adjusts workload placement and coolant flow based on real-time thermal conditions, transitioning from static heat capture to dynamic optimization of energy reclamation efficiency
2Stability of the object's composition
If workload is distributed evenly across racks, then system balance is maintained, but heat concentration for energy harvesting is reduced
Solution Approach 1:
The system creates localized heat concentration zones by selectively placing workloads on specific racks based on thermal conditions and energy harvesting potential, allowing different parts of the data center to have different workload densities to optimize overall energy reclamation
Solution Approach 2:
The adaptive workload management system uses real-time thermal feedback from sensors to dynamically adjust workload distribution, creating a closed-loop control system that balances workload placement with heat harvesting optimization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances energy harvesting by maximizing heat capture and conversion into electrical energy through thermoelectric generators, improving the overall efficiency of energy reclamation in data centers.
Implementation Method 1
Heat generated during operation of electronic components in a data center can be captured and reused for purposes such as heating a building
Data Source
AI summary
Systems, apparatus, and methods for energy harvesting in data centers are disclosed. An example apparatus includes interface circuitry; machine-readable instructions; and at least one processor circuit to at least one of instantiate or execute the machine-readable instructions to estimate first power consumption values for electronic components of a first rack; estimate second power consumption values for electronic components of a second rack; determine a first selection score for the first rack based on the first power consumption values and a second selection score for the second rack based on the second power consumption values; select a first electronic component of the first rack or a second electronic component of the second rack to receive a workload based on the first selection score and the second selection score; and cause the selected one of the first electronic component or the second electronic component to perform the workload.