Vehicle Processor Harvesting via Dispatcher Task Distribution
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Solution Overview
Problem
High performance computational processors in electric, hybrid, and autonomous vehicles remain idle when parked or charging, leading to underutilization of their processing capabilities, which are not effectively harnessed for high performance computing needs.
Innovation Solution
A dispatcher system that receives complex processing requests, parses them into tasks, and distributes them to available vehicle processors within a parking garage or charging facility, prioritizing based on availability and processing power, and returns results to the requestor, while also offering cost reductions for vehicle charging in exchange for processor access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle processors are utilized for high performance computing tasks when parked or charging, then computational power utilization is improved, but system complexity increases due to dispatcher coordination and task distribution mechanisms
Solution Approach 1:
A dispatcher system acts as an intermediary between high performance computing task requestors and idle vehicle processors. The dispatcher receives complex processing requests, parses them into tasks, distributes them to appropriate vehicle processors based on availability and capability, aggregates results, and returns them to requestors. This intermediary layer manages the complexity of coordinating distributed vehicle processors while enabling efficient utilization of idle computational resources.
2Use of energy by moving object
If tasks are distributed to vehicle processors based on charge status and processing power, then energy efficiency is improved, but task distribution complexity increases
Solution Approach 1:
The dispatcher dynamically distributes tasks to vehicle processors based on changing parameters including charge status, processing power availability, and task requirements. By monitoring and responding to these parameter changes, the system optimizes energy efficiency by preferentially utilizing processors with sufficient charge and appropriate computational capabilities, while the automated parameter-based distribution logic manages the complexity of real-time decision-making.
3Productivity
If vehicle processors are harvested for external computing tasks, then resource utilization is improved, but reliability of vehicle systems may worsen due to potential interference with primary vehicle functions
Solution Approach 1:
The system extracts and utilizes idle computational resources from vehicle processors only during periods when the vehicles are parked or charging and not actively performing primary transportation functions. By separating the harvesting of computational resources from the vehicle's primary operational context, the system maximizes resource utilization while minimizing potential interference with vehicle reliability and safety-critical functions.
Data Source
AI summary
Harvesting the high performance computing capabilities of vehicle processors while the vehicles are parked and/or charging may provide use to otherwise unused resources as well as offer a way to discount parking and/or charging costs for the vehicle owner/driver. To harvest the computing capabilities, a dispatcher is utilized that receives incoming complex processing requests and parses the requests into tasks. The dispatcher dispatches the tasks to vehicle processors that are within, for example, a parking garage to which the dispatcher has access. The dispatcher may prioritize requests and/or prioritize the vehicle processors to distribute the tasks to optimize the power consumption, time, and processing capabilities used. When the vehicle processors complete the tasks and return results to the dispatcher, the dispatcher finalizes the job results and provides the results to the requestor. The dispatcher may discount the parking and/or charging costs for the vehicles that performed the computational tasks.