Carbon-Aware Workload Allocation Engine
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Solution Overview
Problem
Current workload allocation models in hybrid cloud environments do not consider carbon emissions as a factor, which is a critical issue for enterprises aiming to reduce their carbon footprint and meet environmental sustainability goals.
Innovation Solution
A method using a workload allocation engine that identifies data centers, servers, and virtual machines, generates server clusters based on predetermined factors, tracks time-series variables, predicts carbon emissions using statistical methods, and assigns workloads to clusters that minimize carbon emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload allocation is based on cost-based optimization model, then resource allocation efficiency is improved, but carbon emission reduction is not achieved
Solution Approach 1:
The patent introduces carbon emission as a new parameter to the workload allocation optimization model, transforming it from a cost-based model to a carbon-aware model. The objective function is modified to minimize carbon emissions by selecting data centers with lower carbon intensity, while still considering resource utilization metrics. This parameter change enables the system to achieve both resource allocation efficiency and carbon emission reduction simultaneously.
2Loss of energy
If workload allocation is based on resource utilization-based optimization model, then resource usage efficiency is improved, but carbon emission consideration is lost
Solution Approach 1:
The patent extends the resource utilization-based optimization model by adding carbon emission intensity as an additional parameter. The objective function combines resource utilization metrics with carbon emission data, allowing the system to allocate workloads to data centers that optimize both resource usage and carbon footprint. This multi-parameter approach resolves the contradiction by making carbon emission a consideration alongside resource efficiency.
3Object-generated harmful factors
If carbon emissions are considered in workload allocation, then environmental sustainability is improved, but allocation model complexity increases
Solution Approach 1:
The patent introduces an intermediary component that collects and processes carbon emission data from multiple data centers, transforming raw environmental data into a format suitable for optimization modeling. This intermediary layer handles the complexity of carbon data integration, allowing the core workload allocation algorithm to focus on optimization logic while the intermediary manages the environmental parameter integration.
Solution Approach 2:
The patent formulates carbon emission as a quantifiable parameter that can be integrated into existing optimization frameworks. By expressing carbon intensity in standardized units and creating a mathematical objective function that combines carbon metrics with resource utilization metrics, the model maintains mathematical tractability while incorporating environmental considerations.
4Object-generated harmful factors
If data centers are selected based on lowest carbon emissions, then carbon footprint is reduced, but resource utilization efficiency may decrease
Solution Approach 1:
The patent creates a multi-objective optimization function that balances carbon emission minimization with resource utilization maximization. The objective function is designed to find optimal workload allocation solutions that achieve both goals simultaneously, rather than treating them as conflicting objectives. This allows the system to select data centers that offer the best compromise between low carbon footprint and high resource efficiency.
Data Source
AI summary
A workload allocation engine is configured to allocate a workload in a cloud architecture. A data center for an enterprise is identified, and for the data center, a plurality of servers within the data center and a plurality of virtual machines (VMs) running on the servers are identified. Based upon at least one predetermined factor, a plurality of clusters of the servers generated. For each of the clusters, a plurality of time-series variables are tracked. For each of the clusters and the workload, carbon emissions generated by a particular cluster and for the workload are predicted. The workload is assigned to the particular cluster based upon the predicted carbon emissions associated with the particular cluster, and the workload is performed by the particular cluster.