Data Center Asset Utilization Forecasting With Feature Clustering
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Solution Overview
Problem
Existing data center management systems struggle to efficiently forecast and manage the utilization of assets to optimize workload allocation, leading to potential underutilization or overutilization, which can affect throughput and response times.
Innovation Solution
A method and system utilizing machine learning models trained through feature clustering operations to analyze data center asset utilization, enabling accurate forecasting of asset utilization and optimizing workload allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data center management systems are used to monitor and allocate assets, then the system structure is simple and easy to operate, but the forecasting accuracy is insufficient leading to underutilization or overutilization of assets
Solution Approach 1:
The patent segments the asset utilization forecasting problem into multiple components: feature extraction from utilization data, clustering analysis to identify patterns, and machine learning model training. This segmentation allows each component to be optimized independently, improving overall forecasting accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing utilization data to extract relevant features and pre-training machine learning models with historical data. This preliminary preparation enables the system to make accurate forecasts when needed, improving measurement precision without increasing operational complexity during actual asset allocation decisions.
2Measurement precision
If machine learning models with feature clustering are implemented to improve forecasting accuracy, then asset utilization forecasting precision is improved, but the computational complexity and training time increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using historical utilization data and performing feature clustering in advance. This allows the system to capture patterns and relationships in the data beforehand, so that when forecasting is needed, the pre-trained models can quickly generate accurate predictions without requiring extensive real-time computation.
Solution Approach 2:
The system performs partial action by focusing machine learning training on the most relevant features extracted from utilization data, rather than processing all possible data dimensions. This selective approach maintains high forecasting precision while reducing the overall training time and computational resources required.
3Productivity
If dynamic asset allocation is implemented based on forecasting, then resource utilization efficiency is improved, but the complexity of workload management increases
Solution Approach 1:
The patent implements feedback mechanisms where actual asset utilization outcomes are continuously monitored and fed back into the machine learning models. This feedback loop allows the system to learn from past allocations, refine its forecasting accuracy, and automatically adjust future asset allocations. The feedback-driven approach improves resource utilization efficiency while keeping workload management complexity manageable through automated decision-making.
Solution Approach 2:
The system applies self-service by enabling automated asset allocation decisions based on machine learning forecasts, reducing the need for manual workload management intervention. The machine learning models autonomously analyze utilization patterns and recommend or execute allocation decisions, improving productivity while minimizing the operational complexity burden on human operators.
Data Source
AI summary
A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring a workload executing on a data center asset; analyzing utilization of the data center asset when the data center asset executes the workload; training a machine learning model using the utilization of the data center asset when executing the workload, the training the machine learning model including performing a feature clustering operation using the utilization of the data center asset to provide separate groups of machine learning features; and, generating a data center asset utilization forecast using the machine learning model.