Server Power Capping for SLA-Aware Data Center Efficiency
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Solution Overview
Problem
Data centers face challenges in balancing power efficiency with performance to meet Service Level Agreements (SLAs) due to dynamic workloads and varying utilization rates, leading to inefficient energy consumption and cooling demands.
Innovation Solution
Implement a power management system that uses predictive analytics to set dynamic power caps based on utilization thresholds, cooling capacity, and volatility, adjusting CPU frequencies and voltages to optimize power consumption while maintaining SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If servers are put in deep idle states to conserve energy, then power consumption is reduced, but restoration time increases and activation energy costs increase
Solution Approach 1:
The patent applies dynamics by implementing dynamic power cap adjustments based on real-time utilization rates and predictive analytics. The system continuously adapts power caps between deep idle states and active states, transitioning servers dynamically rather than statically. This allows the system to optimize the balance between energy conservation and rapid restoration capability based on predicted workload patterns.
2Speed
If server CPU frequency is increased to improve processing speed, then response time is reduced, but power consumption increases due to voltage increase
Solution Approach 1:
The patent implements dynamic frequency and voltage adjustment by continuously monitoring utilization rates and predictive analytics to determine optimal power caps. The system dynamically scales CPU frequency and voltage based on actual workload demands rather than operating at fixed high speeds, thereby reducing power consumption during low-utilization periods while maintaining performance during high-demand periods.
Solution Approach 2:
The patent applies parameter changes by modifying CPU operating parameters (frequency and voltage) based on predicted utilization patterns. The system changes these parameters dynamically according to the determined power caps, optimizing the trade-off between processing speed and power consumption by adjusting parameters rather than maintaining fixed settings.
3Use of energy by moving object
If power cap is reduced to lower power consumption, then energy efficiency is improved, but SLA compliance may be violated during high demand periods
Solution Approach 1:
The patent applies preliminary action by using predictive analytics to forecast future utilization rates and proactively adjusting power caps before peak demand periods occur. The system anticipates high-demand periods and pre-adjusts power caps to ensure SLA compliance is maintained, rather than reacting after SLA violations occur. This allows the system to optimize power consumption while ensuring reliability through advance planning.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual utilization rates and SLA compliance metrics, then using this feedback to adjust power caps dynamically. The system learns from actual performance data and predictive analytics to optimize power cap settings that balance energy efficiency with SLA compliance, creating a closed-loop control system that adapts to changing conditions.
Data Source
AI summary
A system and methods are provided for improving power efficiency of a data center, including: acquiring training data including power caps, utilization rates, and a measure of Service Level Agreement (SLA) compliance of one or more computer servers of the data center; creating a model for determining power caps according to measured utilization rates of the one or more computer servers, wherein the determined power caps, when applied to the one or more computer servers, reduce power consumption and meet the measure of SLA compliance; and applying the model, according to subsequent data received during a second operating period, to determine a power cap to apply to the one or more computer servers, wherein the subsequent data includes a subsequent utilization rate of the one or more computer servers.