Graph-Based Data Center Representation for Energy and Carbon Prediction
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Solution Overview
Problem
Current models for data centers are too rigid and fail to capture the dynamic, heterogeneous nature of modern data centers, leading to inefficiencies in energy consumption and carbon emissions, and existing representations become cumbersome when scaled from single hosts to complete data centers.
Innovation Solution
Model hosts as graphs that include both static and dynamic information, using Graph Neural Networks (GNNs) to process these graphs for flexible and composable predictions on energy consumption and carbon emissions, allowing for optimized workflow configurations and digital twin simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rigid models are used to represent data centers, then the model structure is simple, but the model fails to capture dynamic and heterogeneous nature of modern data centers
Solution Approach 1:
The patent transforms static data center models into dynamic graph representations where nodes and edges can change over time. The graph structure dynamically adapts to represent heterogeneous hardware configurations, software stacks, and operational states, allowing the model to capture the evolving nature of modern data centers while maintaining a unified representation framework.
Solution Approach 2:
The patent segments the data center into discrete graph nodes representing individual components (servers, storage devices, networking equipment) and their relationships. This segmentation allows the model to capture heterogeneity at the component level while composing these segments into a complete data center representation, resolving the contradiction between detail capture and model simplicity.
2Measurement precision
If detailed representations are created for single hosts, then the representation accuracy is high, but the representation becomes cumbersome when scaled to complete data centers
Solution Approach 1:
The patent creates a universal graph representation framework that can represent both individual hosts and complete data centers using the same structural paradigm. The graph model serves multiple functions: it can represent a single server's hardware/software configuration, multiple servers in a rack, or the entire data center, maintaining consistent accuracy while scaling through compositional aggregation of graph structures.
Solution Approach 2:
The patent implements a nested graph structure where host-level graphs are contained within rack-level graphs, which are in turn contained within data center-level graphs. This nesting allows detailed representations at the host level to be composed into larger data center representations without redundancy, maintaining precision while enabling scalable aggregation through hierarchical graph composition.
3Measurement precision
If Graph Neural Networks are used to process graph representations, then prediction accuracy for energy consumption and carbon emissions is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational workload by processing graph representations in a hierarchical manner, starting from individual host graphs and progressively aggregating to rack and data center levels. This segmentation allows GNN computations to be performed on smaller subgraphs first, reducing overall computational complexity while maintaining prediction accuracy through progressive aggregation of results from different hierarchical levels.
Data Source
AI summary
A method includes generating a first data center graph including a plurality of host graphs, the host graphs representing resources of hosts of a data center, combining the first data center graph with an application signature graph to produce a second data center graph, the second data center graph representing the resource utilization of an application when running on the data center, predicting an energy consumption of the application when running on the data center by processing the second data center graph using a graph neural network (GNN), and scheduling the application on the hosts of the data center based on the predicted energy consumption of the application.