Digital Twin Edge Server Switching for Connected Vehicles
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Solution Overview
Problem
Connected vehicles face challenges in determining when to utilize onboard resources versus edge server resources due to latency issues in edge computing, necessitating a switching mechanism to optimize resource usage based on driving contexts.
Innovation Solution
A digital twin-based switching system that determines the best resource usage by comparing current driving contexts with simulated latency data from digital twin simulations to decide whether to use onboard or edge server resources, ensuring efficient computational offloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If edge computing is used to expand computational ability, then computational resources are improved, but latency increases
Solution Approach 1:
The system dynamically switches between onboard and edge server computing resources based on real-time driving context and predicted latency. The switching decision is not static but adapts to changing conditions, selecting the optimal computing location (onboard or edge) for each computational task based on current network conditions and task requirements.
Solution Approach 2:
The system changes the parameter of computing resource location by comparing predicted latency values against thresholds. When predicted latency exceeds the threshold, the system transitions from using edge server resources to onboard resources, effectively changing the operational parameter of where computation occurs based on latency predictions.
2Loss of time
If onboard resources are used, then latency is reduced, but computational ability is limited
Solution Approach 1:
The system dynamically adjusts the use of onboard versus edge resources based on real-time conditions. When edge server latency is predicted to be acceptable (below threshold), the system transitions to using edge server resources, thereby dynamically expanding computational ability when conditions permit.
3Power
If edge server resources are always used, then computational ability is maximized, but switching complexity increases
Solution Approach 1:
The system uses digital twin simulations to create a virtual copy of the edge server environment for prediction purposes. By simulating latency conditions in a digital twin before actual switching decisions, the system simplifies the switching mechanism by relying on pre-computed predictions rather than complex real-time analysis.
Solution Approach 2:
The system performs preliminary latency prediction using digital twin simulations before making switching decisions. By pre-calculating expected latency values for different driving contexts and comparing them against thresholds, the system simplifies the actual switching decision process to a straightforward comparison operation.
Data Source
AI summary
The disclosure includes embodiments that provide a digital twin-based edge server switching decision. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of digital twin data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the determining that the threshold for the predicted latency is satisfied.