Autonomous Driving Simulation Scheduling via Minimum-Cost Flow
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Solution Overview
Problem
Current automatic driving simulation task scheduling systems using traditional queue frameworks struggle with low resource utilization efficiency and high development costs due to their inability to implement complex scheduling strategies for diverse and dynamic simulation tasks.
Innovation Solution
The method employs a graph-based abstraction of the cluster network to represent tasks as a minimum cost flow problem in a flow network, incorporating a fairness strategy and preemptive scheduling to optimize resource allocation, thereby improving scheduling decisions and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional queue framework is used for scheduling automatic driving simulation tasks, then the system structure remains simple, but resource utilization efficiency is low and complex scheduling strategies cannot be implemented
Solution Approach 1:
The patent segments the scheduling system into multiple independent components: a graph construction module that builds the task dependency graph, a minimum cost flow calculation module that computes optimal schedules, and a scheduling execution module that implements the schedules. This segmentation allows complex scheduling strategies to be implemented through modular components without overwhelming system complexity.
Solution Approach 2:
The patent introduces a graph-based minimum cost flow model as an intermediary between task definitions and scheduling decisions. This intermediary transforms the scheduling problem into a mathematical optimization problem, enabling complex scheduling strategies to be implemented through well-established flow network algorithms while maintaining clear system boundaries.
2Productivity
If queue framework scheduling is used, then implementation is straightforward, but global optimal scheduling decisions cannot be obtained and only local optimal solutions are achieved
Solution Approach 1:
The patent replaces the mechanical queue-based scheduling approach with a mathematical optimization approach based on minimum cost flow theory. This substitution enables global optimal scheduling decisions by formulating the problem as finding the minimum cost flow through a constructed graph, rather than relying on simple queue processing rules.
Solution Approach 2:
The patent changes the fundamental parameters of the scheduling system by introducing cost weights on graph edges that represent resource consumption, task priorities, and dependencies. By optimizing the total cost in the flow network, the system achieves global optimality in scheduling decisions, contrasting with the local optimality of traditional queue frameworks.
3Adaptability or versatility
If general framework scheduling system is used, then system compatibility is maintained, but cluster resource utilization is not fully utilized and development costs increase
Solution Approach 1:
The patent performs preliminary actions by constructing a comprehensive graph model that incorporates all task dependencies, resource requirements, and constraints before scheduling execution. This preliminary graph construction enables the minimum cost flow algorithm to optimize resource allocation across the entire task set, maximizing cluster resource utilization before tasks are actually executed.
Data Source
AI summary
An automatic driving simulation task scheduling method and apparatus, a computer device, and a readable storage medium. The method includes: constructing a network flow block diagram on the basis of the number of cluster racks, the number of nodes, and the number of processes, and coding information of the network flow block diagram (S01); setting information of a task to be simulated, and performing shortest path computation on a simulation task flow by means of the network flow block diagram (S02); and updating the state of a scheduling system in real time according to a fair policy and a preemptive scheduling policy, and executing scheduling on the basis of the state of the scheduling system (S03).