Inter-Packet ML Information Exchange in Network Switches
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Solution Overview
Problem
Current network switches in 5G communication systems face challenges in achieving ultra-low latency and high reliability due to the time-consuming process of generating accurate value learning matrices for reinforcement learning, which affects scheduling decisions and overall network performance.
Innovation Solution
The implementation of inter-packet communication in network switches, where machine learning information is exchanged between packets to update value learning matrices, allowing for more efficient scheduling and configuration of network switches, particularly through the use of reinforcement learning and value learning matrices to optimize packet transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network switches generate accurate value learning matrices using reinforcement learning to optimize scheduling decisions, then scheduling performance and network efficiency are improved, but the time required to generate these matrices increases, leading to higher latency
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing value learning matrices in network switches before they are needed for scheduling decisions. This allows the switches to have ready-to-use ML models that can immediately inform scheduling decisions without requiring real-time computation, thus reducing latency while maintaining scheduling optimization benefits
Solution Approach 2:
The patent uses copying by replicating value learning matrices across multiple network switches. Instead of each switch independently computing its own ML model from scratch, switches can share and copy pre-computed matrices, significantly reducing the time required to obtain accurate scheduling information while maintaining the reliability of ML-optimized decisions
2Productivity
If network switches use reinforcement learning to optimize packet scheduling decisions, then network efficiency and throughput are improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs complex reinforcement learning computations in advance to generate value learning matrices, then stores these pre-computed results in network switches. This separates the heavy computational workload (done beforehand) from the actual scheduling decisions (made quickly using stored matrices), thereby maintaining high throughput while reducing real-time computational complexity
Solution Approach 2:
By copying pre-computed value learning matrices across multiple switches, the system eliminates the need for each switch to independently perform complex RL computations. This distribution of pre-computed knowledge reduces individual switch complexity while maintaining overall network productivity
3Speed
If network switches implement inter-packet communication to exchange machine learning information, then the speed of ML information propagation is improved, but the device complexity and overhead increase
Solution Approach 1:
The patent merges the ML information exchange function with the existing packet communication infrastructure. By combining ML data transmission with standard packet forwarding mechanisms, the system achieves fast ML information propagation without adding separate complex communication channels, thus improving speed while minimizing additional device complexity
Solution Approach 2:
The packet communication mechanism is designed to serve multiple functions: it simultaneously handles routine packet forwarding and ML information exchange. This multi-functionality allows the same infrastructure to propagate ML models across the network at high speed without requiring dedicated complex communication hardware, thereby improving propagation speed with minimal overhead
Data Source
AI summary
A network switch includes one or more queues to hold packets received from a first input flow and a second input flow. The network switch also includes a packet communication switch configured to access a first header of a first packet in the one or more queues and a second header of a second packet in the one or more queues. The first header includes first machine learning (ML) information that represents a first set of state transition probabilities under a set of actions performed at the network switch. The second header includes second ML information that represents a second set of state transition probabilities under the set of actions performed at the network switch. The packet communication switch is configured to selectively modify the first header or the second header based on a comparison of the first ML information and the second ML information.