Mobile Vehicle Workload Scheduling for Service-Time Deployment
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Solution Overview
Problem
The challenge of efficiently deploying and managing workloads on computing devices for mobile vehicles, particularly due to URL changes and operating hours, leads to unnecessary consumption of computational, storage, and network resources, as users and devices repeatedly attempt to access unavailable services.
Innovation Solution
A system that detects when a mobile vehicle arrives at a location, determines the start and duration of service time, deploys workloads on suitable computing devices, and provides notifications to user devices, ensuring efficient resource utilization by terminating workloads when the vehicle moves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workloads are continuously deployed on computing devices for mobile vehicles, then service availability is improved, but computational resources are wasted when services are unavailable
Solution Approach 1:
The system dynamically deploys and terminates workloads based on the mobile vehicle's service status. Workloads are deployed when the vehicle arrives at a location and terminated when it departs, making the computational resource allocation dynamic rather than static. This resolves the contradiction by ensuring resources are consumed only when services are actually available.
Solution Approach 2:
The system performs preliminary actions by detecting vehicle arrival and deploying workloads before users attempt to access services. This proactive deployment ensures service availability is established in advance, preventing resource waste from failed access attempts while maintaining reliability.
2Ease of operation
If workloads are deployed based on vehicle location, then service accessibility is improved, but URL changes cause access failures
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring vehicle location and service status. When the vehicle moves or service status changes, the system receives feedback and updates workload deployment accordingly, generating new URLs that reflect current service locations. This resolves the contradiction by ensuring URLs remain valid and services remain accessible.
Solution Approach 2:
The system introduces an intermediary component that manages the relationship between vehicle location, service URLs, and workload deployment. This intermediary translates vehicle position and service status into appropriate workload configurations and URL generations, preventing direct coupling between location changes and access failures.
3Productivity
If workloads are automatically terminated when vehicle moves, then resource efficiency is improved, but service interruption may occur
Solution Approach 1:
The system performs preliminary actions by detecting vehicle departure and initiating workload termination before complete service interruption occurs. This advance termination allows for graceful resource release while maintaining service continuity until the actual departure is complete, resolving the contradiction between efficiency and reliability.
Data Source
AI summary
A server may determine a start time to start executing workloads associated with services provided by a mobile vehicle. The start time may be determined based on historical data and statistical information regarding an amount of preparation time for the mobile vehicle to prepare to provide the services. The server may determine a duration of service time for executing the workloads. The duration of service time may be determined based on data regarding an amount of preparation time for the mobile vehicle to provide the services. The server may determine, based on the start time and the duration of service time, one or more computing devices that are capable of executing the workloads during the duration of service time. The server may cause the one or more computing devices to start executing the workloads at the start time.