Redundant Computing Services for Vehicle Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing technologies in cloud and edge computing for vehicles lack redundancy and scalability, which can lead to safety concerns in safety-critical applications, as they often rely on single hardware and software resources, making them vulnerable to failures and errors.
Innovation Solution
A method that utilizes multiple computing services with different hardware and software resources, implementing redundancy, scaling, load balancing, and error detection and correction mechanisms to ensure reliable and safe data processing, including the use of redundant processing units and resources across edge servers and cloud systems for vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple computing services with different hardware resources are used, then safety and reliability are improved, but device complexity increases
Solution Approach 1:
The system segments computing tasks across multiple independent computing services (e.g., computing service 1, computing service 2) running on different hardware resources (e.g., hardware resource 1, hardware resource 2). Each service processes portions of the computation task independently, allowing the system to achieve redundancy and improved safety without requiring a completely new unified architecture.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple computing services with different hardware resources before safety-critical tasks are executed. This includes pre-establishing communication channels, pre-allocating resources, and pre-validating service configurations, which reduces the complexity of real-time coordination during actual operation.
2Reliability
If redundant computing services are provided, then reliability is improved, but loss of energy increases
Solution Approach 1:
The system applies partial redundancy by providing multiple computing services for safety-critical tasks while allowing single-service operation for non-critical tasks. The degree of redundancy can be dynamically adjusted based on task priority, ensuring that energy is consumed proportionally to the actual safety requirements rather than uniformly across all operations.
Solution Approach 2:
The system dynamically adjusts the number and configuration of active computing services based on real-time requirements. During normal operation, fewer services may be active to conserve energy, while during safety-critical operations or when failures are detected, additional services are activated to provide the necessary redundancy and reliability.
3Adaptability or versatility
If scaling of resources is carried out during operation, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor the performance, load, and health status of computing services. Based on this feedback, the system automatically adjusts resource allocation and service configuration during operation, enabling adaptability through closed-loop control rather than complex open-loop reconfiguration.
Solution Approach 2:
The computing services are designed to be self-managing, automatically detecting their own performance characteristics and self-adjusting their resource requirements. This self-service capability reduces the need for external complex control systems, as each service can independently scale and adapt to changing conditions.
4Productivity
If load balancing is carried out between multiple computing services, then productivity is improved, but device complexity increases
Solution Approach 1:
The system introduces a load balancing intermediary that acts as a mediator between computation tasks and computing services. This intermediary component centralizes the complexity of load distribution logic in a single element, simplifying the overall system architecture while still enabling sophisticated load balancing across multiple services to improve productivity.
Data Source
AI summary
A computer-implemented method for processing data for applications in the field of cloud computing and/or edge computing, for vehicles. The method includes: providing multiple computing services using at least two different hardware resources, and using the multiple computing services.