Rack Power Capping via Node Consumption Profiles
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Solution Overview
Problem
Information handling systems face challenges in seamlessly managing power demands and interruptions, as they need to efficiently allocate power resources across processing nodes within a rack-based system while maintaining system reliability and preventing data loss.
Innovation Solution
A computer-implemented method and rack-based information handling system that enables predictive power capping and power budget allocation at the rack level, using a management controller to receive node-level power usage data, generate power consumption profiles, and adjust power distribution based on actual usage, allowing for selective turning on and off of power supplies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power is allocated to processing nodes based on initial power budgets, then system reliability is maintained, but power efficiency deteriorates due to inability to adapt to actual usage patterns
Solution Approach 1:
The system performs preliminary power allocation by assigning initial power budgets to processing nodes before operation begins. This ensures that sufficient power is available to meet reliability requirements from the start, while the preliminary nature allows for later optimization based on actual usage patterns.
Solution Approach 2:
The management controller continuously monitors actual power consumption of processing nodes and uses this feedback to dynamically adjust power allocation. This feedback mechanism enables the system to optimize power efficiency while maintaining reliability by adapting to real-world usage patterns rather than relying solely on static initial budgets.
2Measurement precision
If power consumption profiles are generated and analyzed for each processing node, then power allocation precision is improved, but system complexity increases due to additional monitoring and analysis requirements
Solution Approach 1:
Each processing node effectively performs self-service by having its power consumption automatically monitored and profiled by the management controller. The system generates power consumption profiles for each node based on actual usage data, enabling precise power allocation without requiring complex manual intervention or additional hardware at the node level.
Solution Approach 2:
The management controller serves multiple functions: it monitors power consumption, generates consumption profiles, analyzes usage patterns, and dynamically adjusts power allocation. This multi-functionality consolidates complexity into a single controller rather than distributing it across multiple components, achieving precise power allocation while managing system complexity centrally.
3Use of energy by moving object
If the system dynamically adjusts power distribution based on actual usage, then power efficiency is improved, but loss of energy increases due to frequent power adjustments and interruptions
Solution Approach 1:
The system implements dynamic power distribution by continuously adjusting power allocation to processing nodes based on their actual power consumption patterns. The management controller modifies power budgets in real-time to match actual usage, improving power efficiency by ensuring power is allocated when needed and not wasted when not required.
Solution Approach 2:
The management controller performs periodic monitoring and adjustment of power allocation rather than continuous modification. By analyzing power consumption profiles at intervals and making staged adjustments, the system improves power efficiency while minimizing energy loss associated with frequent power changes and interruptions.
4Use of energy by moving object
If power supplies are selectively turned on and off based on actual usage, then power efficiency is improved, but reliability deteriorates due to potential power interruptions
Solution Approach 1:
The system performs preliminary assessment of power consumption patterns before turning off power supplies. By analyzing historical and current power usage data, the management controller identifies processing nodes that can tolerate power interruptions or have alternative power sources, enabling selective power supply shutdown while maintaining overall system reliability.
Solution Approach 2:
The management controller continuously monitors system operation and power consumption in real-time, using this feedback to make informed decisions about power supply status. When power supplies are turned off, the controller monitors for signs of stress or performance degradation and can restore power if reliability thresholds are approached, balancing efficiency gains with reliability maintenance.
Data Source
AI summary
A computer-implemented method enables rack-level predictive power capping and power budget allocation to processing nodes in a rack-based IHS. A rack-level management controller receives node-level power-usage data and settings from several block controllers, including current power consumption and an initial power budget for each node. A power consumption profile is generated based on the power-usage data for each node. A total available system power of the IHS is identified. A system power cap is determined based on the power consumption profiles and the total available system power. A current power budget is determined for each node based on an analysis of at least one of the power consumption profile, the initial power budget, the current power consumption, the system power cap, and the total available system power. A power subsystem regulates power budgeted and supplied to each node based on the power consumption profiles and the system power cap.