Rack Power Capping With Forecast-Based Allocation
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Solution Overview
Problem
Existing data processing systems face challenges in efficiently managing power consumption, leading to potential under-allocation or excess power usage, which can impact performance and efficiency.
Innovation Solution
A forecasting analysis is used to predict power consumption, and power caps are optimized using telemetry data and optimization models to ensure adequate power distribution across data processing systems within a rack, managed by a baseboard management controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power caps are set statically without forecasting, then device complexity is reduced, but power allocation efficiency deteriorates leading to under-allocation or excess power usage
Solution Approach 1:
The system performs preliminary power consumption forecasting using telemetry data and machine learning models before allocating power caps. This advance prediction allows the system to pre-determine optimal power distribution, avoiding both under-allocation and excess power usage while maintaining efficient power management without real-time complexity.
2Reliability
If power caps are increased to prevent under-allocation, then reliability improves, but energy waste increases due to excess power usage
Solution Approach 1:
The system continuously collects telemetry data from data processing systems and feeds it back into the machine learning forecasting model. This feedback loop enables the system to dynamically adjust power caps based on actual power consumption patterns, ensuring reliable power allocation while minimizing energy waste through accurate, data-driven predictions.
3Measurement precision
If forecasting analysis is implemented for power consumption, then power allocation accuracy improves, but computational overhead increases
Solution Approach 1:
The system uses machine learning models to create predictive copies of future power consumption patterns based on historical telemetry data. Instead of performing complex real-time calculations, the system relies on pre-trained models that quickly generate accurate forecasts, reducing computational overhead while maintaining high prediction accuracy for power allocation.
Data Source
AI summary
Methods and systems for managing power consumption by data processing systems are disclosed. The power consumption may be managed by forecasting power consumption and optimizing power caps based on the power consumption forecasts. The power consumption may be forecasted by ingesting telemetry data from data processing systems into a power consumption forecasting analysis and obtaining future power consumption forecasts. The power caps may be optimized by ingesting the future power consumption forecasts. After optimization, the power caps may be implemented in the data processing systems.