Leaf-Spine Traffic Forwarding Using Periodic Flow Scheduling
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Solution Overview
Problem
AI large model networks experience traffic congestion due to periodic fluctuations and large data volumes, leading to traffic collisions and congestion at downstream ports.
Innovation Solution
A traffic forwarding method and apparatus that involves a forwarding engine reporting flow statistics information to a processor, merging flow statistics based on timestamps, determining cycle information, and advertising flow characteristic information to leaf nodes for upstream data flow scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple computing nodes simultaneously send traffic to computing nodes under the same Leaf node, then data transmission volume increases, but traffic collisions and congestion occur at downstream ports
Solution Approach 1:
The system performs preliminary actions by collecting flow statistics information in advance and predicting future traffic patterns. The processor determines cycle information and flow characteristic information before congestion occurs, enabling proactive scheduling decisions at leaf nodes to prevent traffic collisions
Solution Approach 2:
The system implements feedback mechanisms where the forwarding engine reports flow statistics information to the processor, which then determines flow characteristics and provides scheduling instructions back to leaf nodes. This closed-loop feedback enables continuous optimization of traffic scheduling based on actual network conditions
2Measurement precision
If flow statistics information is collected and processed in real-time, then traffic scheduling accuracy improves, but processing complexity and time overhead increase
Solution Approach 1:
The system merges similar flow statistics information by determining cycle information and flow characteristic information that group multiple individual flow records into unified patterns. This merging process reduces the volume of data that needs to be processed while maintaining scheduling accuracy
Solution Approach 2:
The system employs periodic action by collecting flow statistics at regular intervals and determining cycle information based on periodic patterns. This approach smooths out transient variations and reduces processing complexity compared to continuous real-time processing
Data Source
AI summary
Disclosed are a traffic forwarding method and apparatus. In an example of the present disclosure, a forwarding engine may report flow statistics information to a processor in response to a determination that an information reporting condition is met. Among flow statistics information with the same flow identification information, the processor may merge the flow statistics information meeting a preset interval duration merging condition based on the first packet timestamp and the last packet timestamp. The processor determines cycle information of a flow based on the merged flow statistics information, determine flow characteristic information of a downstream port of a spine node based on the cycle information of the flow, and advertise the flow characteristic information of the downstream port of the spine node to leaf nodes, so that the leaf nodes perform upstream data flow scheduling based on the flow characteristic information of the downstream port of the spine node.