Self-Learning Data Center Energy Control Using Green Quotients
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Solution Overview
Problem
Data centers face challenges in dynamically increasing energy efficiency as their configurations and applications change, leading to significant energy consumption, despite efforts to source energy from renewable sources.
Innovation Solution
Implementing an edge services platform with an energy efficiency module that determines the green quotient of data centers and application workloads, using machine learning to optimize energy usage by modifying network protocols, scaling port speeds, and activating cooling systems to improve energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data centers source energy from renewable energy sources, then energy sustainability is improved, but the ability to dynamically increase energy efficiency cannot be achieved
Solution Approach 1:
The patent implements dynamic energy efficiency optimization by continuously monitoring workload patterns, traffic conditions, and energy consumption data. The system dynamically adjusts network protocols, scales port speeds, and activates cooling systems based on real-time conditions, transforming static energy management into a dynamic adaptive process that responds to changing data center requirements.
Solution Approach 2:
The data center system performs self-optimization through automated machine learning algorithms that analyze energy consumption patterns and workload characteristics. The system self-adjusts configuration parameters, selects optimal network protocols, and manages cooling without requiring external intervention, enabling autonomous dynamic energy efficiency improvement.
2Adaptability or versatility
If data center configurations and applications constantly change, then adaptability is improved, but energy efficiency cannot be dynamically increased
Solution Approach 1:
The system continuously collects feedback from multiple sources including workload patterns, traffic conditions, and energy consumption data. This feedback loops through machine learning models that analyze the data and generate optimization recommendations, which are then implemented to adjust configurations and reduce energy consumption in response to changing data center states.
Solution Approach 2:
The patent changes multiple system parameters dynamically including network protocol selection, port speed scaling, and cooling system activation thresholds. These parameter adjustments are made based on analyzed workload characteristics and energy consumption patterns, allowing the system to adapt configurations to current conditions and optimize energy efficiency.
3Loss of energy
If machine learning is used to optimize energy usage, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The machine learning system serves multiple functions simultaneously: it analyzes workload patterns, predicts energy consumption, optimizes network protocol selection, determines port speed scaling, and triggers cooling system activation. By consolidating these functions into a unified ML-based optimization platform, the system achieves comprehensive energy efficiency improvement without proportionally increasing complexity.
Data Source
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AI summary
Techniques are described for determining the energy usage of a data center and invoking one or more actions to improve the energy usage of the data center. For example, a computing system may obtain energy usage data of a data center. The computing system may also determine, based on a comparison of the energy usage data of the data center to a percentage of energy provided by one or more renewable energy sources to the data center, a green quotient of the data center that specifies a value that indicates whether the data center is energy efficient. The computing system may further invoke, based on the green quotient of the data center that specifies a value that indicates the data center is not energy efficient, an action to improve energy usage of the data center.