Neural Data Encoding Pattern for Fair QoS Stream Scheduling
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Solution Overview
Problem
Existing data transmission systems face inefficiencies and resource starvation due to uneven distribution of transmission capabilities among end devices, particularly when one device consumes excessive resources, leading to unmet needs and potential starvation for others.
Innovation Solution
A data rearrangement system and method utilizing a neural network model to generate an encoding pattern based on QoS information and scheduling policies, encoding data streams, and decoding them to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling policies (FIFO, SJF, FQ, PRIO) are used to manage data transmission order, then computing efficiency and reliability are improved, but resource starvation occurs when one end device consumes excessive resources
Solution Approach 1:
The patent changes the scheduling parameter from fixed rules to dynamic neural network predictions. The system uses a neural network model to predict future transmission needs and dynamically adjusts scheduling decisions, transforming the scheduling approach from static parameter-based to adaptive parameter-changing scheduling that can prevent resource starvation while maintaining efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors transmission patterns, resource usage, and scheduling outcomes. This feedback is fed into the neural network model to refine predictions and adjust scheduling decisions, creating a closed-loop system that adapts to changing conditions and prevents resource starvation
2Speed
If a single end device continuously transmits large numbers of packets using UDP, then transmission speed is improved, but bandwidth resources are depleted and other end devices cannot meet transmission needs
Solution Approach 1:
The patent applies preliminary action by using the neural network model to predict future transmission requirements before they occur. The system proactively identifies potential resource exhaustion scenarios and adjusts scheduling decisions in advance, preventing bandwidth depletion before it affects other devices rather than reacting after the problem occurs
Solution Approach 2:
The patent introduces dynamics into the scheduling system by making scheduling decisions adaptive and flexible. The neural network model continuously learns from transmission patterns and dynamically adjusts packet prioritization and resource allocation based on current network conditions, allowing the system to respond to changing transmission demands and prevent any single device from monopolizing bandwidth
Data Source
AI summary
A data rearrangement system is provided. The system includes a data-encoding device and a data-decoding device. The data-encoding device is configured to receive one or more data streams from one or more end devices. The data-encoding device is configured to generate a data-encoding pattern based on quality of service (QoS) information, scheduling policy, and transmission information of the data streams using a neural network model. The data-encoding device is configured to encode the data streams to generate an encoded data stream with an encoder, according to the data-encoding pattern. The data-encoding device is configured to transmit the data-encoding pattern and the encoded data stream to the data-decoding device. The data-decoding device is configured to restore the data stream according to the data-encoding pattern and the encoded data stream.