Dynamic Power Budgeting Engine for Chassis Nodes
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Solution Overview
Problem
Information handling systems face challenges in dynamically managing power consumption, leading to inefficiencies and potential overloads, as existing power budgeting methods are static and do not adapt to real-time usage variations.
Innovation Solution
A dynamic power budgeting engine (DPBE) is introduced, which includes chassis level components and computing nodes that manage power supply by determining steady state power consumption ranges, allocating power dynamically, and adjusting power allocations based on real-time monitoring, using a process flow with decision, learning, and action phases to optimize power usage and distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static power budgeting methods are used, then power allocation is simple to implement, but power usage efficiency deteriorates due to inability to adapt to real-time usage variations
Solution Approach 1:
The patent implements dynamic power budgeting by continuously monitoring actual power consumption of computing nodes and adjusting power allocations in real-time based on observed usage patterns. The system transitions from static pre-defined budgets to dynamic adjustments, allowing power allocation to adapt to varying workload conditions and improve overall power usage efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where actual power consumption data from computing nodes is continuously collected and fed back to the power management system. This feedback loop enables the system to learn from actual usage patterns and make informed adjustments to power allocations, resolving the contradiction between simple implementation and adaptive efficiency.
2Adaptability or versatility
If static power budgets are allocated, then power distribution is stable, but adaptability to real-time usage variations deteriorates
Solution Approach 1:
The patent applies dynamics by making power budgets adjustable rather than fixed. The system monitors actual power consumption and dynamically modifies allocations to match real-time demands, enabling adaptability while maintaining stability through controlled, incremental adjustments based on observed patterns.
Solution Approach 2:
The power management system performs self-adjustment by automatically monitoring its own power distribution effectiveness and reallocating power budgets without external intervention. This self-service capability allows the system to adapt to usage variations while maintaining stable operation through autonomous corrective actions.
3Productivity
If manual power management is used, then control precision is high, but human error increases and operational efficiency deteriorates
Solution Approach 1:
The system automates power management tasks by having the power management system itself perform monitoring, analysis, and allocation adjustments without human intervention. This eliminates human error while maintaining operational efficiency through automated decision-making based on real-time data.
Solution Approach 2:
The automated feedback loop continuously monitors power consumption and adjusts allocations based on observed patterns, replacing manual control with reliable automated control. This feedback-driven automation improves operational efficiency while eliminating human error, thereby enhancing reliability.
Data Source
AI summary
A chassis determines a steady state power consumption of each node in the chassis based upon real-time monitoring of power consumption of the nodes. The chassis also determines a power allocation for each node based upon the steady state power consumptions for the nodes. The chassis also determines a total power allocation for the chassis based upon the steady state power consumptions for the nodes. The chassis also determines a source and amount of input power for the chassis based upon the total power allocation for the chassis. The steady state power consumption of a node may be determined by sampling the power consumption of the node during a window period, and setting the steady state consumption of the node to a range if the values of the samples during the window period are within the assigned range.