Building Workload Allocation Across Edge and Cloud for Lower Carbon

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing building management systems face challenges in efficiently managing computing workloads, optimizing machine learning model performance, and enhancing sustainability, particularly in the context of on-premises and off-premises devices, without considering environmental impacts.

Innovation Solution

A method for processing compute activities in building management systems that involves determining device allocation based on workload characteristics and device capabilities, including on-premises and off-premises systems, and optimizing processing times and locations to minimize carbon emissions and energy consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If computing workloads are processed using on-premises building devices, then data security and control are improved, but computing resource limitations and processing capacity are worsened

Engineering Contradiction:

Improvedata security and controlVSAvoidprocessing capacity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent combines on-premises building devices with off-premises computing resources into a unified hybrid architecture. The workload manager distributes computing tasks between local edge devices and remote cloud resources, merging the security benefits of on-premises processing with the scalability of off-premises resources to simultaneously improve reliability and productivity.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces a new dimension of spatial distribution by implementing a multi-layered architecture that operates across different locations (on-premises and off-premises). This dimensional expansion allows the system to overcome the processing capacity limitations of single-location devices while maintaining security controls through distributed architecture.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Productivity

If more computing resources are allocated to processing workloads, then processing speed and capacity are improved, but energy consumption and carbon emissions are worsened

Engineering Contradiction:

Improveprocessing speed and capacityVSAvoidenergy consumption and carbon emissions

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent implements dynamic workload allocation that adjusts resource distribution in real-time based on task priorities, deadlines, and energy considerations. The workload manager dynamically scales computing resources, allocating more power to time-critical tasks and less to non-urgent processing, thereby improving processing speed when needed while reducing energy consumption during low-priority periods.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the operational parameters of computing resources by adjusting voltage frequencies, processing modes, and resource allocation ratios based on workload characteristics. This parameter optimization enables the system to achieve high processing speeds for critical tasks while operating in low-power modes for routine operations, balancing productivity and energy loss.

Inventive Principle:

Principle #35Parameter changes

3Speed

If computing tasks are processed in real-time, then responsiveness and service quality are improved, but energy consumption and processing costs are worsened

Engineering Contradiction:

ImproveresponsivenessVSAvoidprocessing costs

Core Design Contradiction:

SpeedVSUse of energy by stationary object

Solution Approach 1:

The patent segments computing tasks into real-time critical and non-critical categories, processing them through different pathways. Time-sensitive tasks are routed to edge devices for immediate local processing, ensuring responsiveness, while non-urgent tasks are deferred to off-premises resources or batched for later processing, reducing overall energy consumption and costs.

Inventive Principle:

Principle #1Segmentation

4Measurement precision

If machine learning models are continuously retrained to improve performance, then model accuracy is improved, but computing workload and energy consumption are worsened

Engineering Contradiction:

Improvemodel accuracyVSAvoidcomputing workload

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The patent implements a feedback-driven model retraining mechanism that monitors model performance metrics and triggers retraining only when accuracy degradation exceeds predefined thresholds. This feedback control prevents unnecessary continuous retraining, maintaining model accuracy while significantly reducing the computing workload and energy consumption associated with frequent retraining cycles.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260044378A1Building management systems with sustainability improvement architectures

Publication Date: 2026.02.12 TYCO FIRE & SECURITY GMBH

AI summary

A method for processing compute activities of a building management system of a building, the method including receiving, by one or more processors, a selection from a user of a sustainability tuning level from among a plurality of sustainability tuning levels, the plurality of sustainability tuning levels representing different levels of weighting to be placed on mitigating a sustainability impact of computing workloads balanced against one or more other factors, receiving, by one or more processors, a computing workload to be processed by the building system, determining, by the one or more processors, an execution plan for processing the computing workload based at least in part on the selected sustainability tuning level, and causing, by the one or more processors, the computing workload to be processed in accordance with the execution plan.