Building ML Resource Allocation Using Trigger-Based Node Selection
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Solution Overview
Problem
Existing building management systems face challenges in efficiently processing data and deploying machine learning models due to limitations in resource allocation and computational capabilities.
Innovation Solution
The implementation of a distributed machine learning model resource allocation system within building management systems, which utilizes a network of computational resources to evaluate trigger conditions, select appropriate nodes for model deployment, and optimize resource usage based on performance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed in building management systems, then data processing capability is improved, but resource allocation complexity increases
Solution Approach 1:
The patent introduces a resource allocation manager as an intermediary component that sits between the machine learning model deployment system and the building management infrastructure. This manager evaluates trigger conditions, assesses node capabilities, and makes intelligent decisions about model placement, thereby resolving the complexity of resource allocation while maintaining high data processing capability through distributed model execution across multiple nodes
2Productivity
If machine learning models are deployed on multiple nodes, then system performance is improved, but resource selection complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the resource allocation manager continuously monitors node capabilities, workload conditions, and model performance metrics. Based on this feedback, the manager dynamically adjusts model deployment decisions, selecting optimal nodes for execution while balancing system performance requirements with resource availability, thus managing the complexity of multi-node resource selection
3Measurement precision
If trigger conditions are evaluated for model deployment, then model deployment accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary evaluation of trigger conditions and node capabilities before actual model deployment decisions are made. The resource allocation manager pre-assesses whether deployment conditions are met and identifies suitable target nodes in advance, which streamlines the deployment process and reduces processing time while maintaining accurate model placement decisions
Data Source
AI summary
Systems and methods are disclosed relating to distributed machine learning model resource allocation for building management systems. For example, a system can include at least one of an orchestrator or an optimizer to receive data regarding a workload to be performed using one or more machine learning models. The system can process the data to select one or more computing resources to deploy in order to perform the workload. The system can evaluate, using one or more trigger conditions, one or more characteristics of at least one of the data or the workload to determine to perform the workload.