Data Center Management Systems and Methods for Compute Density Efficiency Measurements
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Solution Overview
Problem
Data centers face inefficiencies in energy consumption and resource management due to static infrastructure and unpredictable computing power consumption, leading to challenges in monitoring and optimizing cooling performance, with existing multi-metric views being insufficient for comprehensive cooling effectiveness and future thermal state assessment.
Innovation Solution
A Data Center Infrastructure Management (DCIM) system employing predictive analytics to continuously collect and analyze data from compute, power, and facility systems, enabling automated management and calibration based on estimated compute and power requirements, and incorporating scalable metrics for optimal efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data center density continues to increase, then computing power and capacity improve, but energy consumption and cooling requirements worsen
Solution Approach 1:
The patent implements dynamic workload allocation and infrastructure adjustment mechanisms that allow the data center to adapt computing resources in real-time based on actual demand patterns, preventing over-provisioning and reducing wasted energy consumption while maintaining high computing power utilization
Solution Approach 2:
The system continuously monitors and adjusts operational parameters such as cooling temperatures, power distribution levels, and compute resource allocation based on real-time conditions, optimizing the balance between computing power output and energy consumption by changing these parameters dynamically rather than maintaining static settings
2Productivity
If data center density continues to increase, then computing power improves, but cooling requirements worsen
Solution Approach 1:
The patent employs predictive analytics and machine learning models to forecast future thermal conditions and cooling demands before they occur, allowing the cooling system to be pre-adjusted to optimal settings, preventing thermal buildup and reducing the overall cooling load required to maintain computing operations
Solution Approach 2:
The system implements continuous feedback loops that monitor temperature, heat generation patterns, and cooling system performance in real-time, automatically adjusting cooling parameters based on actual thermal conditions and heat load variations from computing equipment, thereby optimizing cooling efficiency at high density
3Adaptability or versatility
If static infrastructure is placed under dynamic workloads, then operational flexibility improves, but infrastructure inefficiencies worsen
Solution Approach 1:
The patent transforms the static infrastructure into a dynamic system through automated orchestration layers that continuously monitor workload characteristics and adjust resource allocation, power distribution, and cooling parameters in real-time, enabling the infrastructure to adapt flexibly to changing workloads while maintaining optimal efficiency through automated control
Solution Approach 2:
The system creates a multi-functional management platform that handles diverse workload types (compute, storage, networking) across various infrastructure components through unified software control, allowing the same infrastructure to efficiently serve multiple functions and workload patterns without requiring dedicated static configurations for each scenario
4Loss of information
If traditional multi-metric views are used for cooling performance, then broader understanding improves, but comprehensive cooling effectiveness assessment worsens
Solution Approach 1:
The patent merges multiple separate cooling performance metrics (PUE, IT Thermal Conformance, IT Thermal Resilience) into a unified comprehensive assessment framework that evaluates cooling effectiveness holistically, combining these metrics with additional parameters to provide both broad understanding and precise measurement of overall cooling performance rather than treating them as isolated indicators
Data Source
AI summary
Embodiments disclosed include data center infrastructure management (DCIM) systems and methods configured to, collect data center compute systems, power systems, and facility systems data, trigger an action or actions based on a diagnosed or predicted condition according to the collected data, and thereby control via a compute, power, and facilities module, the compute systems, power systems and facility systems in the data center. According to an embodiment, the control via the compute, power, and facilities module comprises calibrating the compute, power, and facility systems based on an estimated compute requirement, and an associated power, cooling, and network data resource requirement. The estimated compute requirement comprises estimating compute density per real-time power wattage, and storage density per real-time power wattage.