ML Scheduling for Data Center Energy Optimization

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Solution Overview

Problem

Data centers face inefficiencies in managing idle servers, leading to significant energy consumption and availability trade-offs, as existing methods for allocating sleep states are often heuristic and retrospective, becoming obsolete at runtime.

Innovation Solution

A machine learning scheduling system that uses historical data to train a model for optimizing resource allocation and sleep state management at runtime, dynamically adjusting server availability and power consumption to minimize energy usage and carbon footprint.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If servers are kept on standby to ensure availability, then service reliability is improved, but energy consumption increases

Engineering Contradiction:

Improveservice availabilityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent implements dynamic sleep state management that adjusts server states based on real-time workload conditions. The system transitions servers between active and sleep states dynamically, rather than maintaining fixed standby states, thereby reducing energy consumption while ensuring availability when needed.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by proactively transitioning servers to sleep states before workload arrives, and waking them up in advance when workload is predicted. This preliminary state management reduces the time servers spend in high-consumption standby states while maintaining service availability.

Inventive Principle:

Principle #10Preliminary action

2Use of energy by moving object

If servers transition to sleep states to reduce power consumption, then energy efficiency is improved, but service availability response time worsens

Engineering Contradiction:

Improvepower consumptionVSAvoidavailability response time

Core Design Contradiction:

Use of energy by moving objectVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms that monitor workload patterns and system state, using this information to dynamically adjust sleep state transitions. The feedback loop ensures that servers wake up in time for actual workload demands, minimizing both energy consumption and availability response time.

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If heuristic methods are used for sleep state allocation, then implementation simplicity is improved, but optimization accuracy deteriorates

Engineering Contradiction:

Improveimplementation simplicityVSAvoidoptimization accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces traditional heuristic mechanical decision-making with machine learning-based intelligent decision-making. The ML model analyzes historical and real-time data to make optimized sleep state allocation decisions, significantly improving optimization accuracy while maintaining computational efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements self-service through automated ML-driven decision-making that continuously learns from historical data and adapts to changing workload patterns. This eliminates the need for manual heuristic tuning while maintaining implementation simplicity through automated model training and deployment.

Inventive Principle:

Principle #25Self-service

4Loss of information

If retrospective analysis is used for resource allocation, then data-driven decision making is improved, but real-time adaptability worsens

Engineering Contradiction:

Improvedata-driven decision qualityVSAvoidreal-time adaptability

Core Design Contradiction:

Loss of informationVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by using historical data to train ML models in advance, capturing retrospective patterns. The trained models then make real-time predictions and decisions without requiring complex retrospective analysis at runtime, thus achieving both data-driven quality and real-time adaptability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements dynamic resource allocation where the ML model continuously adapts to changing workload patterns by incorporating real-time data. The system transitions from static retrospective analysis to dynamic real-time decision-making, improving adaptability while maintaining data-driven decision quality.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20240330047A1Resource aware scheduling for data centers

Publication Date: 2024.10.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Provided is a method, system, and computer program product for using machine learning to allocate resources to workloads at run time in an optimized manner to minimize resource consumption. A processor may generate training data from a retrospective analysis of historical resource management data associated with a computing system. The processor may train a machine learning model to optimize resource management of the computing system at run time using the training data. The processor may obtain optimization recommendations for a current state of the computing system from the machine learning model. The processor may implement the optimization recommendations to manage the current state of the computing system.