Cooling System Output Adjustment via Usage Thresholds
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Solution Overview
Problem
The increasing reliance on cloud-based resources for computing tasks leads to high energy consumption due to the generation of heat and the need for continuous cooling systems, which are often inefficient and wasteful.
Innovation Solution
A system and method that utilize energy management devices to aggregate usage data from computing nodes across different time zones, determining usage thresholds to proactively decrease cooling system output and adjust performance of computing components based on aggregated prior usage data and real-time usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If cooling systems operate continuously at full capacity to maintain computing nodes, then temperature control is ensured, but energy consumption increases
Solution Approach 1:
The cooling system operates dynamically by adjusting fan speeds and cooling intensity based on real-time usage data and temperature conditions. The system transitions from static full-capacity operation to dynamic adaptive operation, matching cooling output to actual computational load and thermal conditions.
Solution Approach 2:
The system implements feedback control by continuously monitoring usage data from computing nodes and adjusting cooling system operation accordingly. Usage thresholds trigger cooling adjustments, creating a closed-loop control system that responds to actual operational conditions rather than operating at fixed capacity.
2Use of energy by moving object
If cooling systems are reduced or shut down to save energy, then energy consumption decreases, but temperature control reliability deteriorates
Solution Approach 1:
The system performs preliminary cooling actions before usage peaks occur by analyzing usage patterns and proactively adjusting cooling output. Usage thresholds are established based on historical data to trigger cooling adjustments in advance, preventing temperature spikes rather than reacting to them.
Solution Approach 2:
The system changes operational parameters by establishing usage thresholds that trigger specific cooling adjustments. Different usage levels correspond to different cooling intensities, creating a parameter-based control strategy that optimizes the balance between temperature control and energy consumption.
3Loss of energy
If usage-based thresholds are implemented to control cooling, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The cooling control system serves itself by using usage data from the computing nodes to automatically determine when cooling adjustments are needed. The system monitors its own operational context and makes self-directed cooling decisions based on usage thresholds, reducing the need for external control complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces energy consumption by optimizing cooling system usage and adjusting computing performance according to actual demand, thereby conserving energy and improving operational efficiency.
Implementation Method 1
the physical computing components that provide these remote resources generate heat and often require cooling systems that require substantial amounts of energy to operate
Data Source
AI summary
Methods and systems for reducing energy consumption. A method may include aggregating, for a prior time period, prior usage data from a plurality of computing nodes. Based on the aggregated prior usage data from the plurality of computing nodes, a usage threshold for decreasing cooling system output for the plurality of computing nodes and a local-time threshold for decreasing the cooling system output for the plurality of computing nodes are determined. Current usage data for the plurality of computing nodes is then received. When the current usage data reaches the usage threshold and the local time is after the local-time threshold, output of the cooling systems of the plurality of computing nodes is decreased.