Screen updating transmission method based on reinforcement learning and fountain codes

A technology for strengthening learning and transmission methods, applied in the field of cloud computing, can solve the problems of reducing the sending window and failing to achieve transmission efficiency, and achieve the effect of enhancing adaptability and reliable transmission

Active Publication Date: 2020-06-19
SICHUAN UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

RL-TCP introduces another effective reinforcement learning method SARSA into TCP NewReno, but TCPNewReno itself multiplies the sending window

Method used

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  • Screen updating transmission method based on reinforcement learning and fountain codes
  • Screen updating transmission method based on reinforcement learning and fountain codes
  • Screen updating transmission method based on reinforcement learning and fountain codes

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Embodiment Construction

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0049] Such as figure 1 As shown, the method of the present invention includes a transmission rate control module based on reinforcement learning and a forward error correction module based on fountain codes, and the specific process is as follows:

[0050] Step 1: On the sending side, the screen update data to be transmitted is segmented and sent to the sending rate control module based on reinforcement learning. This module preserves TCP NewReno's slow-start mechanism to quickly utilize bandwidth after screen update transmission starts or times out. When entering the congestion avoidance stage, the module enables the reinforcement learning method SARSA to learn the optimal sending strategy corresponding to different network states in real time. The network state consists of five parts: the exponentially weighted moving average of ...

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Abstract

The invention discloses a screen update transmission method based on reinforcement learning and fountain codes, and the method employs a reinforcement learning algorithm SARSA to control the transmission rate of screen update, carries out the transmission behavior decision through a specified transmission target instead of a fixed rule, and improves the adaptability of the transmission method to different network conditions. A Gilbert-Elliot model is adopted to model a network packet loss behavior, reliable transmission of screen updating is ensured by means of a system Raptor code and an ARQmechanism, the number of unnecessary retransmission times is reduced by dynamically setting the size of a coding block and the number of redundant packets, and the transmission real-time performance is considered. By means of the method, efficient transmission of screen updating in the desktop service mode is achieved, and the requirement of a user for the experience quality can be better met.

Description

technical field [0001] The invention relates to an end-to-end reliable transmission method under the desktop-as-a-service mode in the field of cloud computing, in particular to a screen update transmission method based on reinforcement learning and fountain codes. Background technique [0002] With the continuous evolution of cloud computing and virtualization technologies, cloud services have penetrated into every aspect of daily life. As an important branch of cloud services, desktop as a service is an emerging and economical cloud service model dedicated to meeting users' needs for remote access to personal desktops. In this service mode, users expect that the remote virtual desktop can retain the full functionality of the local desktop and ensure a good user experience. When a user interacts with a remote virtual desktop, screen updates, as a visual carrier of the user's perceived execution results, play an important role in the quality of experience. Existing research...

Claims

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Application Information

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IPC IPC(8): H04L1/00H04L1/20H04L12/841
CPCH04L1/004H04L1/0056H04L1/0079H04L1/203H04L47/283
Inventor 王俊峰郑弘迪张建平李凡
Owner SICHUAN UNIV
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