Predictive Cloud Presimulation for Network Latency Reduction
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Solution Overview
Problem
Cloud-based digital experiences often suffer from network latency issues, which can degrade the user experience due to the need for real-time feedback on user inputs, and existing methods for distributing tasks between client and cloud-based services are inflexible and time-consuming, especially when dealing with fluctuations in network and hardware performance.
Innovation Solution
Implementing predictive cloud-based presimulation, where a server simulation runs concurrently and ahead of a client simulation, allowing for the selective sending of simulation results based on likelihood of use, thereby optimizing task distribution between client and cloud-based services to minimize latency and adapt to user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based services are used to leverage greater computing resources, then processing power and simulation quality are improved, but network latency and user experience degradation occur
Solution Approach 1:
The system performs presimulation of future game states on the cloud server before the client actually needs the data. By predicting which simulation results will be needed and computing them in advance, the system eliminates network latency for those specific data transfers, as the results are already available locally when needed.
Solution Approach 2:
The system dynamically adjusts the simulation timeline and data transfer schedule based on predicted client needs. Rather than static periodic updates, the presimulation adapts to changing game states and client requirements, optimizing when and what data is transferred to minimize latency impact.
2Ease of manufacture
If traditional task distribution methods are used between client and cloud, then implementation is straightforward, but flexibility and adaptability to performance fluctuations are reduced
Solution Approach 1:
The system performs presimulation of future game states on the cloud server before the client actually needs the data. By predicting which simulation results will be needed and computing them in advance, the system eliminates network latency for those specific data transfers, as the results are already available locally when needed.
Solution Approach 2:
The system continuously monitors client performance, network conditions, and simulation state to dynamically adjust presimulation parameters. This feedback loop enables the system to adapt to changing conditions in real-time, optimizing the balance between cloud computing utilization and client-side performance requirements.
3Loss of information
If all simulation results are sent from server to client, then client has complete data availability, but network bandwidth consumption and transmission time increase
Solution Approach 1:
The system extracts and transfers only the specific simulation results that are predicted to be needed by the client, rather than transmitting all possible data. This selective data transfer maintains necessary data availability while significantly reducing network bandwidth consumption and transmission time for unused data.
Solution Approach 2:
The system performs presimulation of future game states on the cloud server before the client actually needs the data. By predicting which simulation results will be needed and computing them in advance, the system eliminates network latency for those specific data transfers, as the results are already available locally when needed.
Data Source
AI summary
Embodiments related to predictive cloud-based presimulation are described herein. For example, one disclose embodiment provides, on a computing device, a method comprising receiving an input of state from a client device and executing a server simulation of a digital experience based on the input of state, the server simulation configured to run concurrently with, and ahead of, a client simulation on the client device. The method further comprises generating a plurality of simulation results from the server simulation, selecting one or more simulation results from the plurality of simulation results based on a likelihood the client simulation will utilize a particular simulation result, and sending the one or more simulation results to the client device.