Asynchronous Node-Based Time-Interval Data Structures for Scalable Multi-Agent Simulation
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Solution Overview
Problem
Current simulation platforms are unable to accurately simulate large-scale cyber-physical systems (CPS) environments with hundreds of thousands or millions of agents due to limitations in computing resources, which do not scale linearly with the number of agents, making it computationally infeasible to model complex systems like modern cities.
Innovation Solution
The implementation of systems and methods that utilize in-memory and in-process databases for flat query-processing, asynchronous node-based time-interval structures, and atomic increment/decrement operations to improve scalability and efficiency, allowing for the simulation of complex CPS environments with near-linear processing times and reduced thread contention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If known simulation platforms are used to simulate CPS environments, then the simulation can be executed with current systems, but the number of agents is limited to about 40,000 and cannot scale to hundreds of thousands or millions of agents
Solution Approach 1:
The system segments the simulation environment into multiple sectors, each containing a subset of agents. This allows the overall simulation to be divided into manageable parts that can be processed independently, enabling scaling to millions of agents across multiple sectors while maintaining control over computational complexity in each segment.
Solution Approach 2:
The patent introduces a new dimension of organization by grouping agents into sectors and using hierarchical data structures. This dimensional organization transforms the flat, unmanageable list of individual agents into a structured hierarchy that enables efficient processing and scaling to large numbers of agents.
2Measurement precision
If the number of agents is increased to simulate large-scale environments, then the realism and accuracy of the simulation improves, but the computing resources required do not scale linearly and become computationally infeasible
Solution Approach 1:
By segmenting agents into sectors and using asynchronous processing within each sector, the system maintains high simulation accuracy with hundreds of thousands or millions of agents while preventing processing efficiency from degrading. Each sector can be processed independently, allowing parallel execution that maintains productivity despite increased scale.
Solution Approach 2:
The system implements dynamic sector assignment and asynchronous processing that adapts to the current simulation state. This dynamic approach allows the simulation to maintain accuracy across varying scales while optimizing processing efficiency by adjusting which agents are actively processed at any given time.
3Quantity of substance
If more computing resources are allocated to simulate additional agents, then the simulation capacity increases, but the non-linear scaling causes diminishing returns and eventual computational infeasibility
Solution Approach 1:
Segmenting the simulation into sectors with asynchronous processing eliminates the non-linear resource consumption by allowing independent parallel execution. This segmentation ensures that adding computing resources linearly increases simulation capacity without the diminishing returns caused by centralized processing bottlenecks.
Solution Approach 2:
Each sector operates with a degree of autonomy, managing its own agents and processing requirements. This self-service approach allows sectors to efficiently utilize available computing resources without requiring centralized coordination, reducing overall resource consumption while maintaining the ability to simulate millions of agents.
Data Source
AI summary
Disclosed herein are systems and methods for simulating an environment over a plurality of intervals of in-simulation time by using a two-part data structure to represent agents or entities in the simulation. A system may create and store a data structure representing an agent in the simulation, wherein a first portion of the data structure represents characteristics of the agent at a present interval and a second portion of data structure represents characteristics of the agent at a future interval of the simulation. Also disclosed herein are systems and methods for executing a multi-agent simulation using multiple processing threads. A system may group a plurality of agents in the simulated environment into subsets associated respectively with nodes of the simulated environment, assign the subsets to respective separate processors of the system, and execute the simulation using separate processing threads associated respectively with the separate processors.