Phase-Based Virtual Machine Resource Scheduling for Real-Time Vehicle Systems
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Solution Overview
Problem
In vehicle systems, dynamic resource allocation can make it difficult to guarantee real-time responsiveness due to challenges in verifying the feasibility of resource allocation times.
Innovation Solution
An information processing device that creates multiple virtual machines, including a management virtual machine, and sets resource allocation times based on detected phases such as startup, normal, and sleep phases, allowing for static scheduling by phase to ensure real-time responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic resource allocation is implemented to improve adaptability, then resource allocation flexibility is improved, but real-time responsiveness verification becomes difficult
Solution Approach 1:
The system segments resource allocation into phase-specific schedules (startup phase, normal phase, sleep phase) with fixed allocation patterns for each phase. This segmentation allows verification of real-time responsiveness within each phase while maintaining overall adaptability through phase transitions.
Solution Approach 2:
The system implements dynamic resource allocation by switching between different fixed schedules based on detected phases. The detection unit identifies system phases, and the setting unit applies appropriate fixed schedules, achieving adaptability through dynamic phase-based schedule selection while maintaining verifiability within each phase.
2Reliability
If static scheduling by phase is implemented to guarantee real-time responsiveness, then real-time responsiveness is improved, but resource allocation flexibility is reduced
Solution Approach 1:
The system combines static phase-specific schedules with dynamic phase detection and transition. Each phase has a fixed, verifiable schedule, but the system dynamically switches between phases based on runtime conditions, achieving both real-time guarantees and adaptability.
Solution Approach 2:
The system changes scheduling parameters (allocation times) based on detected phase parameters. Different phase parameters (startup, normal, sleep) trigger different scheduling configurations, allowing the system to adapt resource allocation to operational conditions while maintaining fixed schedules within each phase.
3Speed
If CPU allocation time is increased for management virtual machine during startup phase, then startup speed is improved, but resource availability for other virtual machines is reduced
Solution Approach 1:
The system performs preliminary actions by allocating sufficient CPU time to the management virtual machine during the startup phase to ensure rapid initialization. This preliminary focus on management VM startup enables the system to quickly reach a state where other virtual machines can be properly initialized and serviced.
Solution Approach 2:
The system uses periodic phase-based scheduling where the management virtual machine receives prioritized CPU allocation during specific phases (startup and sleep phases). This periodic prioritization ensures fast startup and wake-up while allowing other virtual machines to receive resources during normal operation phases.
Data Source
AI summary
An information processing device includes: a memory; and a processor coupled to the memory, the processor being configured to generate plural virtual machines including a management virtual machine that manages the plural virtual machines, detect predetermined plural phases, and set resource allocation times for the plural virtual machines to predetermined schedules for each of the plural phases based on the detection result.