Data Center Load Distribution With Predictive Thermal Control
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Solution Overview
Problem
Data centers face significant energy inefficiencies due to the rapid growth of energy dissipation in servers and cooling systems, with traditional solutions being reactive and failing to optimize energy use across computing and cooling resources effectively.
Innovation Solution
A holistic approach treating energy as a first-class resource, using fast thermal models and global schedulers to allocate energy budgets to servers based on workload and thermal conditions, combined with proactive and reactive control mechanisms to manage cooling and computing solutions dynamically, ensuring energy-efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If servers are overprovisioned with high capacity, then peak performance is improved, but energy efficiency deteriorates due to operating below optimal load levels
Solution Approach 1:
The patent implements dynamic load balancing that continuously monitors and adjusts workload distribution across servers in real-time, transforming the static overprovisioned architecture into a dynamic system that adapts to actual load conditions, thereby improving energy efficiency while maintaining peak performance capability
Solution Approach 2:
The system changes operational parameters by adjusting the distribution of processing loads across servers based on current energy efficiency metrics and performance requirements, optimizing the operating point of each server to balance productivity and energy consumption
2Productivity
If more servers are packed into data centers, then processing capacity is improved, but energy dissipation and cooling requirements worsen
Solution Approach 1:
The patent applies preliminary thermal modeling to predict future thermal conditions and energy dissipation patterns before workloads are fully executed, allowing the system to proactively adjust load distribution and cooling strategies to minimize energy dissipation while maintaining processing capacity
Solution Approach 2:
The system uses thermal models as simplified copies or representations of the complex thermal behavior of servers, allowing energy dissipation predictions and optimizations without requiring full-scale physical thermal simulations or measurements for every scenario
3Device complexity
If reactive cooling solutions are used, then thermal management is simplified, but energy efficiency deteriorates due to lag in responding to thermal changes
Solution Approach 1:
The patent implements preliminary thermal modeling that predicts future thermal conditions before they occur, allowing the cooling system to be controlled proactively based on predictions rather than reacting to past thermal states, thereby improving cooling energy efficiency while maintaining manageable system complexity
Solution Approach 2:
The system incorporates feedback mechanisms where thermal model predictions are continuously compared with actual thermal measurements, and the model parameters are adjusted accordingly, creating a closed-loop control system that improves cooling efficiency without excessive complexity
Data Source
AI summary
A method for controlling a data center, comprising a plurality of server systems, each associated with a cooling system and a thermal constraint, comprising: a concurrent physical condition of a first server system; predicting a future physical condition based on a set of future states of the first server system; dynamically controlling the cooling system in response to at least the input and the predicted future physical condition, to selectively cool the first server system sufficient to meet the predetermined thermal constraint; and controlling an allocation of tasks between the plurality of server systems to selectively load the first server system within the predetermined thermal constraint and selectively idle a second server system, wherein the idle second server system can be recruited to accept tasks when allocated to it, and wherein the cooling system associated with the idle second server system is selectively operated in a low power consumption state.