Scheduler Simulation for Autonomous Vehicle Software Builds
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Solution Overview
Problem
Current infrastructure struggles to efficiently support a large number of autonomous vehicle (AV) simulations and software builds, as existing scheduling systems lack the ability to understand how various factors influence scheduler decisions and resource utilization, leading to inefficiencies and increased costs.
Innovation Solution
A simulation engine is developed to evaluate and predict the performance of schedulers by simulating scheduling scenarios, considering factors like queue states, resource availability, and task priorities, allowing for optimized resource allocation and reduced costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing scheduling systems are used to manage AV simulations and software builds, then the system can operate with simple scheduling logic, but the infrastructure cannot efficiently support a large number of simulations and builds, leading to increased costs and reduced productivity
Solution Approach 1:
The patent creates a simulation environment that copies the actual scheduler's decision-making process and resource allocation behavior. By modeling the scheduler's logic and creating virtual representations of resources and tasks, the system can analyze scheduler performance without adding complexity to the actual production scheduler. This allows evaluation of scheduling efficiency under various conditions while maintaining the simplicity of the original system.
Solution Approach 2:
The simulation engine acts as an intermediary between the actual scheduler and the infrastructure resources. It models and predicts scheduler behavior, allowing the system to evaluate scheduling decisions and optimize resource allocation indirectly. This intermediary layer enables analysis of scheduler performance and prediction of resource utilization patterns without directly modifying the production scheduling system.
2Productivity
If the infrastructure supports more AV simulations and software builds, then productivity increases, but infrastructure costs increase due to inefficient resource utilization
Solution Approach 1:
The simulation engine provides feedback about scheduler performance and resource utilization patterns by analyzing simulated scheduling scenarios. This feedback information is used to optimize the actual scheduler's resource allocation decisions, improving the efficiency of infrastructure usage. By continuously refining scheduling decisions based on simulated performance data, the system can handle higher volumes of simulations and builds with the same infrastructure resources, reducing costs.
Solution Approach 2:
The system changes key parameters related to resource allocation and scheduler behavior based on simulation results. By adjusting scheduling parameters, resource allocation strategies, and priority assignments according to insights from simulated scenarios, the infrastructure can efficiently support larger volumes of simulations and builds without proportionally increasing costs.
3Productivity
If the scheduler makes more informed decisions about resource allocation, then resource utilization improves, but the ability to predict and understand scheduler behavior becomes more difficult
Solution Approach 1:
The simulation engine creates a copy of the scheduler's decision-making logic and resource allocation behavior. This virtual model can be extensively analyzed, measured, and predicted without affecting the actual production scheduler. By working with the simulation copy, the system can detect patterns, measure performance metrics, and predict future behavior under various conditions, making the scheduler's behavior more transparent and manageable.
Solution Approach 2:
The patent replaces direct observation and measurement of the production scheduler with a simulated model. Instead of trying to directly detect and measure the complex scheduler behavior in the production environment, the system uses a simulation model that replicates the scheduler's mechanics. This substitution allows for easier detection, measurement, and prediction of scheduler behavior while maintaining accuracy through the simulated representation.
Data Source
AI summary
Systems and methods for evaluating schedulers that schedule jobs(s) and/or tasks(s) related to vehicle software builds and/or vehicle simulations are provided. A computer-implemented system, including one or more processing units; and one or more non-transitory computer-readable media storing instructions, when executed by the one or more processing units, cause the one or more processing units to perform operations including receiving a configuration including a simulated request for executing a task associated with at least one of a vehicle software build or a vehicle simulation of a vehicle; executing a simulation of operations of a scheduler and task execution, wherein the executing includes determining, by the scheduler, a schedule for executing the task based on the configuration and at least one of a driving scenario or a vehicle compute framework associated with the task; and calculating a metric for the scheduler based on an output of the simulation.