A short traffic real-time optimization method, system, and network transmission terminal

Abstract

The invention belongs to the technical field of cloud data center network transmission flow scheduling, discloses a short-flow real-time optimization method, system, and network transmission terminal, and constructs a reinforcement learning framework for a data center short-flow real-time optimization method; data center network flow feature acquisition , used to monitor traffic characteristics in the data center and generate traffic size distribution; multi-level queue demotion threshold initialization, calculate the multi-level queue demotion threshold according to the data center internal traffic size distribution characteristics; threshold dynamic adjustment, used for fine-grained dynamic adjustment threshold size In order to adapt to the change of data center traffic size, realize the priority transmission of data packets. The invention realizes adaptive matching of traffic size and transmission based on the reinforcement learning threshold, reduces the transmission delay of interactive real-time short traffic, improves the delayed transmission of long traffic with high bandwidth requirements in the queue, and improves the real-time performance of network transmission.

Description

technical field [0001] The invention belongs to the technical field of cloud data center network transmission flow scheduling, and in particular relates to a short flow real-time optimization method, system, and network transmission terminal. Background technique [0002] At present, data center network traffic transmission optimization is one of the core research issues of cloud environment data centers, and it is a focus of attention and research in the industry and academia. Due to the current differentiation and dynamic characteristics of network traffic in data centers, data The performance optimization of central network traffic transmission has become a very challenging problem. The research on network traffic transmission has always aimed at reducing the traffic completion time and improving the real-time performance of transmission. Cloud applications such as web search, social network and service recommendation all exist in the data center in the form of short traf...

AI Technical Summary

Problems solved by technology

The above existing technologies, due to the characteristics of dynamic changes and suddenness of data center traffic, there is a mismatch between the degraded thresholds of queues at all levels and the size of the traffic, and the optimization period is long, so they cannot automatically adapt to traffic changes. When it does not match the size of the traffic, the priority transmission efficiency of short traffic is low, and there is still the problem that short traffic is delayed by long traffic

[0004] Through the above analysis, the problems and defects of the existing technology are: there is a mismatch between the degraded thresholds of the queues at all levels and the traffic size in the existing technology, and the optimization period is long, so it cannot automatically adapt to the traffic changes. When the size does not match, the transmission efficiency of short traffic is low, and there is still the problem that short traffic is delayed by long traffic

[0005] The difficulty of solving the above problems and defects is: how to fine-grainedly adjust the demotion threshold of multi-level queues to adapt to the dynamic changes in data center traffic volume, and improve the real-time performance of short traffic transmission to be solved

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