Power Management System for Data Center Resource Optimization
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Solution Overview
Problem
Current systems face high operational costs due to ongoing power consumption of idle resources, as they are typically kept powered on to be immediately available for peak workloads, leading to underutilization and increased electrical costs, especially in data centers.
Innovation Solution
A power management system that collects historical utilization data to predict future resource usage, dynamically configuring resources into power-saving modes during low utilization periods and ensuring they are ready before peak times, thereby minimizing power consumption without impacting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are kept powered on to be immediately available for peak workloads, then system availability and responsiveness are improved, but power consumption and operational costs increase
Solution Approach 1:
The system performs preliminary actions by predicting future resource utilization based on historical data and proactively powering resources on before they are needed. This allows the system to maintain availability without keeping all resources continuously powered on, thereby reducing power consumption while ensuring resources are ready when required.
Solution Approach 2:
The system dynamically adjusts the power state of resources based on predicted utilization patterns. Instead of a static on/off state, the system continuously monitors historical data, predicts future needs, and adapts resource availability in real-time, optimizing the balance between availability and power consumption.
2Productivity
If resources are provisioned for peak workload capacity, then system capacity and performance are improved, but resource utilization efficiency deteriorates due to underutilization during non-peak periods
Solution Approach 1:
The system dynamically provisions resources based on predicted workload patterns rather than maintaining static peak capacity. By analyzing historical utilization data and forecasting future needs, the system adjusts resource allocation to match actual demand, ensuring sufficient capacity during peak periods while minimizing resource usage during non-peak periods.
Solution Approach 2:
The system changes the operational parameters of resources by adjusting their power states and activation times based on predicted utilization. This allows the system to maintain peak capacity when needed while operating at reduced capacity during non-peak periods, optimizing the balance between system capacity and resource utilization efficiency.
3Use of energy by moving object
If resources are powered down during low utilization periods, then power consumption is reduced, but system responsiveness deteriorates if resources are not available when needed
Solution Approach 1:
The system performs preliminary actions by predicting when resources will be needed and powering them on in advance. This ensures that resources are available and responsive when actually required, while still allowing the system to power down resources during confirmed low-utilization periods, thus maintaining responsiveness without excessive power consumption.
Solution Approach 2:
The system uses feedback from historical utilization data to continuously refine its predictions and adjust resource power states. By monitoring actual usage patterns and comparing them with predictions, the system learns to accurately anticipate resource needs, ensuring responsiveness is maintained only when necessary while minimizing power consumption during low-utilization periods.
Data Source
AI summary
A method comprises collecting utilization data for a resource, and predicting by a power management system, based on the collected utilization data, future utilization of the resource. The method further comprises controlling, by the power management system, power to the resource, based at least in part on the predicted future utilization of the resource. In one embodiment, the utilization data is collected for a plurality of resources that are operable to perform tasks, and the method further comprises determining, by the power management system, how many of the resources are needed to provide a desired capacity for servicing the predicted future utilization of the resources for performing the tasks. The method further comprises configuring, by the power management system, ones of the resources exceeding the determined number of resources needed to provide the desired capacity in a reduced power-consumption mode.