Point-Based Risk Scoring for Data Center Cooling Allocation
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Solution Overview
Problem
Data centers face challenges in uniformly distributing redundancy and risk across environmentally controlled spaces, leading to potential over-representation or under-representation of cooling capacity, which can result in increased energy costs or catastrophic failures.
Innovation Solution
Calculating reserve and risk values for locations within data centers using influence models and metrics to allocate environmental maintenance modules and load, ensuring targeted placement of new modules and efficient resource allocation based on actual capacity and risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If excess HVAC units are deployed throughout a data center to ensure redundancy, then reliability is improved, but device complexity and energy costs increase
Solution Approach 1:
The patent segments the data center into multiple thermal zones with distinct risk profiles, allowing redundancy to be allocated locally rather than uniformly across the entire facility. This enables targeted placement of HVAC units in high-risk areas while reducing complexity in low-risk areas.
Solution Approach 2:
The patent implements local quality by assigning different redundancy levels to different locations based on their specific thermal characteristics and risk metrics. High-risk locations receive greater redundancy while low-risk locations receive minimal redundancy, optimizing the overall system.
2Reliability
If excess HVAC units are deployed throughout a data center to ensure redundancy, then reliability is improved, but energy costs increase
Solution Approach 1:
The patent applies partial action by deploying HVAC units only to the extent necessary for each specific location's risk profile. Rather than uniformly over-provisioning the entire data center, the system calculates minimum adequate redundancy for each zone, reducing total energy consumption while maintaining reliability.
3Ease of operation
If uniform redundancy is applied across a data center, then ease of operation is improved, but risk representation becomes inaccurate leading to either over-representation or under-representation of cooling capacity
Solution Approach 1:
The patent implements dynamics by making redundancy allocation adaptive and responsive to changing conditions. The system continuously monitors thermal characteristics and risk metrics, dynamically adjusting HVAC unit placement and operation levels to match actual needs rather than relying on static uniform allocation.
Data Source
AI summary
Methods are provided for calculating a reserve value or a risk value for various locations in an environmentally-controlled space such as a data center, and using the reserve value or risk value to allocate environmental maintenance modules and/or load. For example, an influence model can predict a change in a sensor value at a location for a corresponding change in an operation level of an actuator of one of the environmental maintenance modules. Based on the influence model and an operation level of the actuator, a reserve value can be determined for the location. A risk value for the location can be determined using a risk metric that may use the reserve value, a current sensor value at the location, and a threshold sensor value at the location.