Simulation State Rewind by Input Relevancy Distance
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Solution Overview
Problem
Computer simulations, such as multi-player games, face performance degradation due to large input packets and heavy burdens on simulation servers when updating the state for numerous entities, leading to user experience issues like lag and errors.
Innovation Solution
Implementing local prediction on client devices based on user inputs and constraining the amount of simulation state to rewind only for entities within a determined relevancy distance, reducing the number of entities that need to be rewound and simulating componentized entities to minimize network traffic and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the host provider network processes inputs for all entities in the simulation, then the simulation state is accurately updated, but the server load increases and performance degrades
Solution Approach 1:
The patent applies local quality by determining a relevancy distance for each input and selectively rewinding simulation state only for entities within that distance. This allows different parts of the simulation to be processed at different levels of detail - entities close to the input source are fully updated while distant entities are not, optimizing the balance between accuracy and performance.
Solution Approach 2:
The patent segments the simulation state management by dividing entities into those within relevancy distance and those outside it. The system processes inputs differently for these segments - full state rewind for relevant entities, minimal or no rewind for irrelevant entities - thereby reducing overall server load while maintaining necessary accuracy.
2Reliability
If the client device sends complete input packets to the host provider network, then all entity states can be updated, but network traffic increases and latency increases
Solution Approach 1:
The patent extracts only the necessary information for state updates by calculating relevancy distance and selecting only entities within that distance for processing. This extraction approach reduces the amount of data that needs to be transmitted and processed, thereby reducing network latency while maintaining completeness for relevant entities.
Solution Approach 2:
The patent applies partial action by processing only the subset of entities within relevancy distance rather than all entities in the simulation. This partial processing reduces network traffic and processing time while maintaining sufficient accuracy for the affected entities, accepting that distant entities will have less frequent updates.
3Reliability
If the simulation updates state for all entities, then the simulation is accurate, but the burden on simulation servers increases
Solution Approach 1:
The patent implements local quality by applying different processing levels to different entities based on their distance from the input source. Entities within the relevancy distance receive full state updates, while entities outside this distance receive minimal or no updates, reducing server processing complexity while maintaining accuracy where it matters most.
Solution Approach 2:
The patent introduces dynamic processing by calculating relevancy distance for each input and adjusting the scope of state rewind accordingly. This dynamic approach allows the system to adapt its processing complexity based on the specific input and current simulation state, optimizing the balance between accuracy and server burden.
Data Source
AI summary
A simulation environment (e.g., multi-player game) hosted by a provider network may reduce the amount of state data that needs to be rewound when performing simulation and verification of locally predicted entity states from a client device (backward reconciliation). When the simulation server receives an input packet, it determines the relevancy distance of the inputs specified by the input packet. Based on the relevancy distances and a previous state of the simulated entity corresponding to a timestamp, a volume of space is determined. The server identifies entities within the volume of space and only rewinds the state for those entities before performing backward reconciliation.