Risk-Aware Task Offloading Under Unreliable Network Latency
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Solution Overview
Problem
Autonomous devices in production environments face challenges with high computing demands due to limited processing and storage capacities, necessitating distributed computing arrangements that require reliable, low-latency communication infrastructure, which is often not fully achievable.
Innovation Solution
A method for task offloading from autonomous devices that considers safety and network performance, using reinforcement learning to dynamically decide whether to offload computing tasks to remote devices based on risk assessment and network quality of service, incorporating risk evaluation and network KPIs to optimize task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computing tasks are offloaded to remote computing devices, then computing power and task execution capability are improved, but network dependency and latency risks increase
Solution Approach 1:
The system dynamically switches between local and remote execution modes based on real-time network conditions and task characteristics. The decision to offload is not static but adapts to changing environmental factors, allowing the system to optimize between computing power utilization and network reliability concerns.
Solution Approach 2:
The system changes the execution parameter (local vs. remote) based on multiple factors including network latency, task complexity, and safety requirements. By adjusting this parameter dynamically, the system can leverage remote computing power when conditions permit while maintaining reliability when network conditions deteriorate.
2Loss of time
If computing tasks are performed locally, then network latency and communication overhead are reduced, but device processing capacity and task accuracy are limited
Solution Approach 1:
The system segments tasks based on their computational requirements and latency sensitivity. Complex, computationally intensive tasks are candidates for remote offloading, while simple, time-critical tasks are executed locally. This segmentation allows the system to optimize each task's execution location based on its specific characteristics.
Solution Approach 2:
The system performs partial computation locally and offloads only the necessary portions to remote devices. This partial action approach allows the system to maintain real-time responsiveness for critical functions while leveraging remote computing power for non-time-critical but computationally demanding operations.
3Productivity
If more computing resources are allocated to autonomous devices, then task execution capability is improved, but device complexity and deployment cost increase
Solution Approach 1:
The system extracts computationally intensive processing functions from autonomous devices and relocates them to remote computing infrastructure. This extraction allows autonomous devices to maintain simpler, more cost-effective hardware while still achieving high task execution capabilities through cloud-based processing power.
Solution Approach 2:
The remote computing infrastructure serves multiple autonomous devices simultaneously, providing universal computing resources that benefit all connected devices. This multi-functionality approach is more cost-effective than equipping each device with dedicated high-performance computing resources.
4Ease of manufacture
If distributed computing arrangement is implemented, then deployment cost is reduced, but network load and communication requirements increase
Solution Approach 1:
The system implements partial offloading rather than complete redistribution of all computing tasks. By selectively offloading only appropriate tasks to remote devices, the system reduces deployment costs for individual devices while avoiding excessive network load that would result from offloading all computations.
Data Source
AI summary
A method of operating a device that interacts with a physical environment includes determining to take an action on the physical environment wherein the action is based on an output of a computing task, generating a risk level that estimates a risk of physical harm associated with taking the action within the physical environment, obtaining a performance indicator of a communication network between the device and a remote computing device, and determining, based on the risk level and the performance indicator, whether to offload the computing task to the remote computing device or to perform the computing task locally. Related devices are also disclosed.