Vehicle Computational Resources for Scheduled Cloud Computing
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Solution Overview
Problem
Existing cloud computing infrastructure struggles to efficiently utilize the computational resources of connected vehicles, which are typically reserved for their own operations and not leveraged for external cloud computing services due to limited high-speed connectivity and energy availability.
Innovation Solution
A system that enables connected vehicles to offer cloud computing services by utilizing high-speed data connections, such as 5G Millimeter Wave, to transfer data for processing when available, and manage resource availability schedules through a mobile processing allocation platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle computational resources are reserved exclusively for vehicle operations, then vehicle operational reliability is ensured, but cloud computing service availability is limited
Solution Approach 1:
The system dynamically adjusts vehicle resource allocation between vehicle operations and cloud computing services based on real-time conditions. The mobile processing allocation platform monitors vehicle state, network availability, and service demands to flexibly allocate computational resources, allowing the same resources to serve different purposes at different times without compromising vehicle reliability.
Solution Approach 2:
The vehicle's computational resources are segmented into dedicated portions for vehicle operations and shareable portions for cloud computing services. The mobile processing allocation platform creates virtualized resource pools that separate critical vehicle functions from external service requests, ensuring that vehicle operations always have guaranteed resource allocation while enabling cloud services to utilize remaining capacity.
2Speed
If high-speed data connections are used for cloud computing, then data transfer speed is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic data transfer and processing cycles rather than continuous high-speed connection. The mobile processing allocation platform schedules cloud computing tasks to execute during periods when high-speed connectivity is available, then transfers results during lower-energy periods. This periodic approach allows the vehicle to utilize 5G Millimeter Wave connections efficiently without sustained energy consumption.
Solution Approach 2:
The system dynamically changes connection parameters including data transfer rate, connection mode, and processing intensity based on available energy levels and network conditions. When energy is abundant, high-speed data transfer is enabled; when energy is constrained, the system switches to lower-power modes or defers non-critical cloud computing tasks, optimizing the trade-off between speed and energy consumption.
3Productivity
If vehicle computational resources are allocated to cloud computing services, then service productivity increases, but vehicle operation performance may be compromised
Solution Approach 1:
The mobile processing allocation platform performs preliminary assessment of vehicle state and service requirements before allocating resources. It pre-allocates guaranteed resource levels for vehicle operations and only assigns cloud computing tasks to resources above this threshold. This preliminary action ensures vehicle performance requirements are met before any cloud service productivity gains are pursued.
Solution Approach 2:
The system implements continuous feedback monitoring of vehicle operational performance and resource utilization. The mobile processing allocation platform receives real-time data on vehicle system performance and dynamically adjusts cloud computing resource allocation in response. If vehicle operation performance degrades, the platform automatically reduces or pauses cloud computing tasks to restore optimal vehicle operation, ensuring productivity gains do not compromise reliability.
Data Source
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Figure 2A
Figure 2B~2C
AI summary
An approach is provided for enabling remote use of a vehicle's computational resources via wireless network data connection(s). The approach involves, for example, determining, by one or more processors, location data associated with a mobile computing system. The approach also involves processing, by the one or more processors, the location data to determine a transmission availability schedule for transmission of data to and/or from the mobile computing system via one or more wireless network connections. The approach further involves generating, by the one or more processors, a processing availability schedule for the mobile computing system based at least on the transmission availability schedule. The processing availability schedule indicates when a computational resource of the mobile computing system is available for a remote use that is initiated over the one or more wireless network connections. The approach further involves providing, by the one or more processors, the processing availability schedule as an output.