Dynamic Power Capping for Virtualized Data Centers
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Solution Overview
Problem
In data processing centers, static power management policies lead to inefficient power usage due to mismatched power requirements across data center nodes, resulting in unnecessary power loss or resource underutilization, especially in virtualized environments where workload migration occurs dynamically without intelligent power adjustments.
Innovation Solution
A dynamic power capping system that uses multivariate time series forecasting to predict future compute loads and adjust power settings autonomously, smoothing out single point spikes and continuously improving accuracy to ensure optimal power usage across data center nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static power settings are manually configured at each data processing node, then power requirements can be set, but power efficiency deteriorates due to mismatched power requirements across nodes and inability to adapt to dynamic workload changes
Solution Approach 1:
The patent applies dynamics by transitioning from static manual power settings to dynamic automated power management. The system continuously monitors workload metrics and automatically adjusts power settings in real-time, enabling power requirements to adapt to changing workload conditions. This resolves the contradiction by making power settings both efficient (through optimization) and adaptable (through continuous adjustment).
Solution Approach 2:
The patent implements self-service by enabling data processing nodes to automatically manage their own power settings without manual intervention. The system uses local workload monitoring and automated decision-making algorithms to adjust power settings autonomously, eliminating the need for manual configuration while improving both efficiency and adaptability simultaneously.
2Ease of manufacture
If uniform power requirements are applied across all data processing nodes, then configuration is simplified, but resource utilization deteriorates due to mismatched power needs of different nodes
Solution Approach 1:
The patent applies local quality by enabling each data processing node to have customized power settings tailored to its specific workload characteristics and performance requirements. Instead of uniform configuration, the system allows localized optimization at each node while maintaining centralized management capabilities, thus improving resource utilization without significantly increasing configuration complexity.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting power settings based on monitored workload metrics and performance parameters. The system automatically modifies power consumption parameters in response to changing conditions, enabling each node to operate at optimal efficiency levels while maintaining simplified overall system management through automated parameter tuning.
3Adaptability or versatility
If power settings are frequently adjusted to match workload changes, then adaptability improves, but system stability deteriorates due to frequent power setting changes
Solution Approach 1:
The patent applies preliminary action by implementing predictive analytics that forecast future workload conditions and proactively adjust power settings before actual workload changes occur. This approach enables the system to adapt to workload changes smoothly without reactive frequent adjustments, maintaining stability while improving adaptability through forward-looking power management decisions.
Solution Approach 2:
The patent implements periodic action by establishing structured intervals for power setting reviews and adjustments based on workload patterns. Instead of continuous or frequent changes, the system uses periodic evaluation cycles to determine when power adjustments are necessary, reducing unnecessary changes while maintaining adequate adaptability to workload variations.
Data Source
AI summary
A system for data processing, comprising a plurality of data center nodes, each data center node having an associated power requirement. A dynamic power capping system operating on a processor and coupled to the plurality of data center nodes, wherein the processor is configured by the dynamic power capping system to implement one or more algorithms that cause the processor to determine a power requirement for each of the plurality of data center nodes and to implement a power setting at each of the plurality of data center nodes that corresponds to the power requirement for the data center node.