Data Center Power Management via Computational-Electrical Correlation
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Solution Overview
Problem
Current power management systems in data centers fail to optimize the balance between computational power utilization and electrical power supply, leading to inefficient energy usage, with a significant portion of energy being wasted on cooling infrastructure and lacking accurate correlation between computational processing power and electrical consumption.
Innovation Solution
A comprehensive, dynamic power management system that integrates environmental and computational power monitoring, using coordinated monitoring and control to optimize energy usage by correlating power consumption with business service utilization, employing techniques such as CPU-level power management, dynamic load allocation, and virtualization to minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If comprehensive environmental and computational power monitoring is integrated, then power utilization efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces a power management appliance as an intermediary device that collects computational power metrics from servers and electrical power metrics from power distribution panels. This mediator correlates the two types of data to identify relationships between computational workload and electrical consumption, enabling optimization without requiring direct integration between all system components.
Solution Approach 2:
The power management appliance serves multiple functions: it monitors computational power utilization, measures electrical power consumption, correlates these metrics, identifies optimization opportunities, and controls power distribution. This multi-functional approach consolidates what could be separate specialized systems into a single versatile platform.
2Loss of energy
If CPU-level power management techniques are applied, then energy consumption is reduced, but ease of operation decreases
Solution Approach 1:
The system enables servers to self-report their computational power metrics through embedded agents, and the power management appliance automatically correlates this data with electrical consumption measurements. The system autonomously identifies optimization opportunities and implements control decisions without requiring manual intervention, making the complex CPU-level management transparent to operators.
Solution Approach 2:
The patent implements a closed-loop feedback system where the power management appliance continuously monitors both computational and electrical metrics, correlates them to identify relationships, and adjusts power distribution based on the correlations. This automated feedback mechanism reduces energy consumption while eliminating the need for manual power management operations.
3Use of energy by moving object
If dynamic load allocation and virtualization are employed, then power utilization efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic load allocation where the power management appliance continuously monitors computational power metrics and electrical consumption, identifies correlations between workload patterns and power usage, and dynamically adjusts power distribution and load allocation accordingly. This dynamic approach optimizes power utilization efficiency by adapting to changing conditions rather than relying on static configurations.
Data Source
AI summary
A system and method of increasing the efficiency of overall power utilization in data centers by integrating a power management approach based on a comprehensive, dynamic model of the data center created with integrated environmental and computational power monitoring to correlate power usage with different configurations of business services utilization, with the techniques of CPU level power management.