Real-Time Power Efficiency Management for Computing Devices
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Solution Overview
Problem
Existing power management systems for computing devices and datacenters struggle to maintain peak power efficiency due to static energy efficiency curves that fail to account for dynamic workload changes and hardware configurations, leading to inefficient resource utilization and high costs in recalibration processes.
Innovation Solution
Implementing a method that analyzes input and output characteristics of computing devices to determine a composite conversion efficiency metric, setting an indicator for peak efficiency, and dynamically assigning workloads to ensure real-time operation at optimal power consumption levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static energy efficiency curves are used to determine operating power efficiency, then the curves can be generated using past data without real-time monitoring, but the curves become outdated and fail to reflect dynamic workload changes and hardware configurations
Solution Approach 1:
The patent transitions from static efficiency curves to dynamic real-time efficiency monitoring. The system continuously measures input and output power characteristics and calculates efficiency metrics in real-time, allowing the efficiency data to adapt dynamically to changing workloads and hardware configurations without requiring manual curve regeneration.
Solution Approach 2:
The patent implements a feedback mechanism where real-time power consumption data is continuously monitored, analyzed, and used to adjust workload assignments. The system feeds back efficiency metrics to the workload management system, enabling dynamic optimization of power efficiency based on current operating conditions rather than relying on outdated static curves.
2Measurement precision
If cumbersome hardware and communication interfaces are added to each server to collect dynamic data for recalibration, then real-time efficiency monitoring becomes possible, but the cost and complexity increase significantly for datacenters with thousands of servers
Solution Approach 1:
The patent leverages existing multi-functional components already present in servers. The management controller, existing power measurement circuits, and communication interfaces are utilized for dual purposes: their original functions plus real-time efficiency monitoring. This eliminates the need for dedicated separate hardware for each monitoring function, reducing overall system complexity and cost.
Solution Approach 2:
The system enables servers to self-monitor and self-report their own power efficiency characteristics using their existing internal resources. Each server uses its own management controller and existing sensors to measure and report efficiency data, eliminating the need for external monitoring hardware and reducing the burden on central management infrastructure.
3Productivity
If workloads are distributed en masse to servers without considering energy efficiency, then resource utilization appears high, but power consumption increases and peak efficiency is not achieved
Solution Approach 1:
The patent implements dynamic workload distribution that adapts to real-time efficiency conditions. Rather than static or bulk workload assignment, the system continuously monitors efficiency metrics and dynamically adjusts workload assignments to maintain operation at or near peak efficiency points, responding to changing conditions in real-time.
Solution Approach 2:
The system uses real-time efficiency feedback to guide workload distribution decisions. The management system receives continuous efficiency data from servers and uses this feedback to optimize where new workloads are assigned, directing them to servers operating at peak efficiency rather than simply distributing workloads uniformly or based on capacity alone.
Data Source
AI summary
Systems and methods for operating computing devices at peak power efficiency in real time are disclosed. According to an aspect, a method includes analyzing a set of input and output characteristics of a component operable on a computing device servicing one or more workloads. The method also includes determining whether an efficiency metric associated with the component is met based on the set of input and output characteristics. Further, the method includes setting an indicator in response to determining that the efficiency metric is met. Further, the method includes assigning additional workload to another computing device based on whether the indicator is set.