Position Information Filtering With ML Feedback to Cut Latency
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Solution Overview
Problem
The exchange of substantial amounts of user position data in a virtual space leads to decreased operational speeds and potential latency issues due to unnecessary data transmissions in conventional systems.
Innovation Solution
A server uses a machine learning model to determine whether to provide position information to client devices, updating the model based on feedback messages to optimize data transmission, thereby reducing unnecessary data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If position information is exchanged between all client devices in a virtual space, then user interaction and collaboration are improved, but system performance deteriorates due to increased data transmission volume
Solution Approach 1:
The server receives feedback information from client devices about whether position data was actually needed, and uses this feedback to train the machine learning model. This feedback mechanism allows the system to learn from actual usage patterns and optimize future data transmission decisions, resolving the contradiction by adapting to real user needs while maintaining high system performance.
Solution Approach 2:
The system dynamically changes the parameter of data transmission by using a machine learning model to predict whether position information should be sent. Instead of a fixed approach of always transmitting data, the system adjusts transmission behavior based on predicted user needs, thereby reducing unnecessary data exchange while maintaining adequate user interaction capabilities.
2Reliability
If position information is transmitted to all client devices, then data completeness is ensured, but unnecessary data transmissions increase causing latency
Solution Approach 1:
The feedback mechanism allows the system to verify whether transmitted position data was actually useful to the receiving client device. This feedback loop enables the machine learning model to learn from actual outcomes and adjust future transmission decisions, ensuring data completeness only when needed while avoiding unnecessary transmissions that cause latency.
Solution Approach 2:
The machine learning model performs preliminary prediction before data transmission to determine whether position information is likely to be needed. This preliminary action filters out unnecessary transmissions in advance, reducing latency while maintaining data completeness for cases where position information is actually required.
3Productivity
If a machine learning model is used to filter position information, then data transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The feedback mechanism provides a structured way for client devices to communicate their needs to the server, which then uses this information to train the machine learning model. This feedback approach simplifies the overall system by using straightforward client-server communication to achieve intelligent data filtering, rather than requiring complex distributed decision-making algorithms across all devices.
Solution Approach 2:
The machine learning model acts as an intermediary component between the server and client devices, centralizing the complexity in a dedicated filtering layer. This intermediary approach allows the server to manage data transmission efficiency through a single trained model rather than requiring each client device to implement complex decision logic, thereby improving productivity while keeping individual device complexity manageable.
Data Source
AI summary
The embodiments of the disclosure provide a method and system for managing position information, and a computer readable storage medium. The method includes: receiving, by a server, first position information from a first client device; determining, by the server, whether to provide the first position information to a second client device by using a machine learning model; in response to determining to provide the first position information to the second client device, sending the first position information to the second client device; and in response to determining that a feedback message corresponding to the first position information has been received from the second client device, updating, by the server, the machine learning model according to the feedback message.