Automated Power Distribution Management for Datacenter Resilience

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Solution Overview

Problem

Conventional power management systems in datacenters require manual intervention to prioritize servers during power outages, which can lead to catastrophic results if essential servers are not running for extended periods.

Innovation Solution

An automated power distribution management system using machine learning techniques to analyze performance data and generate a priority list for hardware components, ensuring that power is distributed based on component importance during outages, utilizing an ARIMA time series model for real-time prioritization and optimization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual analysis and prioritization of servers is performed during power outages, then administrators can identify critical servers, but the process is time-consuming and may result in extended downtime for essential services

Engineering Contradiction:

Improveavailability of critical serversVSAvoiddowntime during power crisis

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary prioritization of servers during normal operation by analyzing performance data, workload metrics, and service dependencies. This pre-established priority framework enables immediate automated decision-making during power outages, eliminating the time-consuming manual analysis phase while ensuring critical servers are identified based on objective criteria rather than human judgment under pressure.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The power management system automatically monitors its own operational context, analyzes performance data, and makes autonomous decisions about power allocation during outages. The system serves itself by detecting power crises, evaluating server priorities using pre-configured criteria, and executing power distribution decisions without requiring external human intervention, thus reducing both response time and potential human error.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated machine learning techniques are used to prioritize hardware components, then power distribution is faster and more consistent, but the system complexity increases

Engineering Contradiction:

Improvespeed of power distribution decision-makingVSAvoidcomplexity of power management system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system replaces manual mechanical processes (administrators physically analyzing and configuring power priorities) with automated computational processes using machine learning algorithms. The ML models process performance data and automatically generate priority rankings, substituting human cognitive work with algorithmic computation that operates faster and without fatigue, while the complexity is encapsulated in software rather than requiring complex hardware modifications.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11327545B2Automated management of power distribution during a power crisis

Publication Date: 2022.05.10 DELL PROD LP

AI summary

A method comprises analyzing performance data of a system using one or more machine learning techniques. The system comprises a plurality of hardware components. In the method, a priority list of the plurality of hardware components is generated based on the analysis, and power from one or more power sources is distributed to one or more of the plurality of hardware components based on the priority list.