A network load balancing system and a balancing method based on deep reinforcement learning

A technology of reinforcement learning and network load, which is applied in the field of computer networks, can solve problems such as limited load balancing effect, uneven network congestion, and inability of ECMP to effectively solve traffic distribution, so as to achieve load balancing and simple structure

Active Publication Date: 2018-12-18
NANJING ZHAOSHICHANG NETWORK TECH
View PDF4 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] On the other hand, ECMP cannot effectively solve the problem of network congestion caused by uneven traffic distribution
A specific manifestation is that in a network with a symmetrical topology (such as a data center network), due to the sy

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A network load balancing system and a balancing method based on deep reinforcement learning
  • A network load balancing system and a balancing method based on deep reinforcement learning
  • A network load balancing system and a balancing method based on deep reinforcement learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0106] In an embodiment of this solution, we established a simple network model with an asymmetric topology to test whether the DQN model can learn a traffic scheduling policy that is beneficial to network load balancing. The network topology of the model is as follows figure 2 shown.

[0107] Among them, seven nodes A, B, C, D, E, F, and G are set as edge nodes, which are marked as black in the figure as the source node for sending traffic and the destination node for receiving traffic in the network.

[0108] As relay nodes in the network, R1, R2, R3, R4, R5, R6, R7, R8 and R9 do not generate traffic themselves, but can receive and forward traffic from other nodes, which are marked in white in the figure.

[0109] We stipulate that the initial state described above is used as a starting point, and 25 steps are executed as a round, and a total of 10,000 rounds are run.

[0110] As a comparison of this scheme, we simulated the random strategy scheme in the same environment....

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a network load balancing system and a balancing method based on depth reinforcement learning. The balancing system comprises a control plane and a data plane. The control planecomprises an INT module and a network module. The INT module obtains network information on each node in the network module by sending a detection packet and sends the network information to the control plane. The control plane comprises a DQN module, a network state information module, a shortest path routing algorithm module and a node source route path update module. The network state information module receives the network information sent by the control plane and sends the network information to the DQN module. The output action of DQN module calls dijkstra algorithm module to calculatethe optimal path, and transmits the update result of node flow table to the corresponding node equipment in the network. This scheme is based on P4's INT technology and Deep Q Network model in artificial intelligence to achieve intelligent load balancing of SDN network, so as to realize the rational utilization of network resources, effectively improve network efficiency and reduce network congestion.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a network load balancing method based on deep reinforcement learning. Background technique [0002] INT (In-band Network Telemetry) is a framework designed to collect and report network status. It is implemented through the data plane without the intervention of the control plane. In INT's architectural model, data packets contain header fields called "probe commands" by network devices. The instructions corresponding to these fields tell the INT-enabled device what status information it needs to collect and write this information into the INT packet. INT traffic sources (could be applications, network endpoints, etc.) can embed these commands in normal or INT packets. Similarly, the INT traffic sink (traffic sink) collects the results of these instructions to accurately monitor the status of the data plane. [0003] Reinforcement learning is an important machine lea...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H04L12/803G06N99/00
CPCH04L47/125Y02D30/50
Inventor 潘恬黄韬杨凡魏亮刘江张娇杨帆谢人超刘韵洁
Owner NANJING ZHAOSHICHANG NETWORK TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap