Distributed Edge-Cloud System for Customizable Location Services
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The customization of location-based services onboard vehicles is limited, restricting user experience and functionality, as existing systems offer limited options for service configuration and execution.
Innovation Solution
A distributed processing system comprising edge devices and cloud computing devices with core components that communicate and synchronize to provide customizable location-based services, allowing users to select services such as stateful pipelines or microservices, and manage data caching and execution between devices, including user token-based secure operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If location-based services are provided with fixed configuration, then system complexity is reduced, but adaptability and user experience are limited
Solution Approach 1:
The system segments location-based services into independent, modular service units that can be individually selected and configured. Each service operates as a discrete component within the distributed processing system, allowing users to customize their service portfolio without affecting the overall system structure. This modular approach enables service adaptation while maintaining manageable system complexity through standardized interfaces and configuration mechanisms.
2Adaptability or versatility
If more service options are provided, then user experience is enhanced, but system complexity and resource requirements increase
Solution Approach 1:
The distributed processing system implements universal service delivery mechanisms that can handle multiple service types through common infrastructure components. The system uses standardized service interfaces, unified configuration protocols, and shared resource management that work across diverse service options. This multi-functionality approach allows the system to support numerous service types without proportionally increasing complexity, as the same architectural patterns and management mechanisms apply regardless of the specific service portfolio.
3Speed
If services are executed locally on edge devices, then response time is improved, but device resource consumption increases
Solution Approach 1:
The system dynamically determines service execution locations based on real-time conditions including device resource availability, service requirements, and performance targets. Services can be flexibly allocated between edge devices and cloud infrastructure, with the ability to migrate or reconfigure execution locations as conditions change. This dynamic resource orchestration allows the system to optimize the balance between local execution speed and resource consumption, executing services locally when performance is critical and offloading when resources are constrained.
4Speed
If data is cached locally, then access speed is improved, but memory requirements increase
Solution Approach 1:
The system implements selective data caching strategies where different types of data are cached at different locations based on their access patterns and importance. Frequently accessed location data is cached locally on edge devices for rapid access, while less frequently accessed data remains in cloud storage. The system uses intelligent cache management that considers data size, access frequency, and device memory constraints to optimize the distribution of cached data across the distributed infrastructure, thereby improving access speed for critical data without uniformly increasing memory requirements across all devices.
Data Source
AI summary
A distributed processing system for providing location based services is provided along with a system, method and computer program product for customizing services, such as location based services to be provided onboard a vehicle. The distributed processing system includes a plurality of computing devices including at least one edge device and at least one cloud computing device. Each computing device includes a core component and one or more services. The services may be configured as a pipeline or as microservices. The core component of each computing device is configured to communicate with the one or more services of the respective computing device as well as with the core component of at least one of the other computing devices in order to share data, such as data having a conflict-free replicated data type, and synchronize the core components.