Workload Carbon Footprint Scoring for Datacenter Power
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Solution Overview
Problem
Datacenters contribute significantly to global carbon emissions due to high electricity consumption, and existing methods lack a consistent and accurate way to calculate power consumption and carbon footprint at the workload level across servers, network devices, and storage systems.
Innovation Solution
A system that calculates a carbon footprint impact score by determining power consumption per workload across servers, switches, routers, and storage systems, using metrics like IOPS, bandwidth, CPU utilization, and idle power, and normalizes scores across multiple datacenters to facilitate action on reducing carbon footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If total energy consumption is used to calculate carbon footprint, then the calculation is simple, but the accuracy and granularity of carbon footprint assessment is insufficient
Solution Approach 1:
The patent segments the carbon footprint calculation from aggregate datacenter level down to individual workload level. It divides the datacenter into servers, storage systems, and network devices, and further divides each device into multiple workloads. This segmentation enables precise measurement of carbon footprint for each workload while maintaining a structured calculation framework that manages complexity through hierarchical organization.
Solution Approach 2:
The patent creates a universal carbon footprint calculation framework that can be applied across different device types (servers, storage, network) and workload types. The system uses a common set of metrics (power consumption, IOPS, bandwidth, CPU utilization) and a unified calculation methodology that works universally across the entire datacenter infrastructure, enabling consistent carbon footprint assessment regardless of the specific device or workload.
2Measurement precision
If detailed workload-level metrics are collected, then carbon footprint accuracy improves, but data collection and processing complexity increases
Solution Approach 1:
The system implements a universal metrics collection framework that gathers multiple types of data (power consumption, IOPS, bandwidth, CPU utilization) using a consistent approach across all devices and workloads. This universal methodology simplifies the complexity of data collection by applying the same principles throughout the datacenter, making the measurement process more manageable despite the detailed granularity required.
Solution Approach 2:
The patent introduces intermediary components that facilitate metrics collection and processing. These intermediaries aggregate and process raw data from multiple sources, transforming complex device-level metrics into standardized workload-level data that can be used for carbon footprint calculation. This intermediary layer simplifies the overall measurement process by handling data normalization and aggregation.
3Adaptability or versatility
If carbon footprint scores are normalized across multiple datacenters, then comparability improves, but the normalization process complexity increases
Solution Approach 1:
The patent applies parameter changes by introducing normalization factors that transform raw carbon footprint values into comparable scores. The system changes the parameter scale from absolute energy consumption to normalized carbon footprint scores, enabling direct comparison across different datacenters. This parameter transformation maintains versatility and comparability while managing complexity through standardized normalization relationships.
4Productivity
If per-workload power consumption is calculated, then resource utilization optimization becomes possible, but calculation complexity increases
Solution Approach 1:
The patent segments power consumption calculation to the workload level, dividing total device power into individual workload contributions. This segmentation enables precise identification of which workloads consume the most power, allowing for targeted optimization strategies. The hierarchical segmentation from datacenter to device to workload maintains calculation manageability while enabling detailed productivity analysis.
Solution Approach 2:
The system applies local quality by providing detailed carbon footprint and power consumption information for each specific workload rather than treating all workloads uniformly. This localized information enables targeted optimization actions for high-consumption workloads while leaving low-consumption workloads unchanged, improving overall resource utilization efficiency without requiring complex system-wide changes.
Data Source
AI summary
The technology described herein is directed towards determining a datacenter's power consumption of its devices at the workload level, from which an objective carbon footprint impact score can be determined. Devices can include servers, network devices such as switches, and storage devices. For a group of workloads at a location, workload power consumption values can be determined based on collected power-related workload metrics data. The power consumption values are used in determining per-workload carbon footprint values for the workloads based on the location. One or more actions can be taken to modify the respective carbon footprint values, e.g., moving a workload to a different location, changing device hardware, and so on.