ML Inference Offloading via Carbon Footprint Evaluation
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Solution Overview
Problem
Current communication systems face challenges in efficiently offloading inference steps of machine learning models while considering environmental impact, specifically carbon emission footprints, across various host nodes in a network.
Innovation Solution
An apparatus and method for receiving requests to offload inference steps of machine learning models to host nodes in a network, acquiring information related to the application, machine learning model, and host nodes, including carbon emission footprints, to determine optimal deployment options and provide indications of these options to entities in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inference steps are offloaded to host nodes, then processing capacity and service quality are improved, but carbon emission footprint increases
Solution Approach 1:
The system changes the parameter of energy source selection by evaluating carbon emission footprints of different host nodes and selecting nodes with lower carbon emissions for inference task offloading, thereby reducing the harmful factor while maintaining processing capacity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring carbon emission footprints of host nodes and using this information to dynamically adjust offloading decisions, optimizing the balance between processing capacity and environmental impact
2Productivity
If multiple host nodes are evaluated for offloading, then deployment optimization is improved, but system complexity increases
Solution Approach 1:
The system segments the evaluation process by dividing host nodes into categories based on their characteristics (e.g., carbon emission footprint, processing capacity, location) and evaluating them separately, which simplifies the overall complexity while maintaining comprehensive optimization
Solution Approach 2:
The system uses a universal evaluation framework that can assess multiple host nodes based on common criteria (carbon footprint, capacity, availability), making the complex evaluation process manageable through standardized procedures
Data Source
AI summary
There is provided an apparatus comprising means for: receiving a request to offload at least one inference step of a ML model for an application to a host node of a network comprising a plurality of host nodes, acquiring first information related to the application, acquiring second information related to the ML model, acquiring third information related to at least one host node, wherein the third information comprises at least an indication of a carbon emission footprint associated with at least one host node, determining, based on the first information, the second information and the third information, at least one deployment option of at least one inference step to offload on at least one host node and providing an indication of the determined at least one deployment option to the second entity from the first entity.