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Multi-objective disaster backup method and system between data centers based on reinforcement learning

A data center, disaster backup technology, applied in digital transmission systems, transmission systems, data exchange networks, etc., can solve the problems of daily service impact of data centers, failure to consider network link load balancing, etc., to alleviate maximum link congestion , the effect of slowing down the maximum link congestion and reducing bandwidth waste

Active Publication Date: 2021-05-28
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, for redundant disaster backup, most studies use multicast routing to reduce backup bandwidth consumption, but most of them do not consider the load balancing of network links. Daily services will also be severely affected

Method used

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  • Multi-objective disaster backup method and system between data centers based on reinforcement learning
  • Multi-objective disaster backup method and system between data centers based on reinforcement learning
  • Multi-objective disaster backup method and system between data centers based on reinforcement learning

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Experimental program
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Embodiment 1

[0030] At present, for redundant disaster backup, most studies use multicast routing to reduce backup bandwidth consumption, but most of them do not consider the load balancing of network links. Daily services will also be severely affected. However, applying the store-and-forward mechanism in the time-expanding network can better solve the problem of link congestion and achieve link load balancing. Due to the rise of software-defined networks, traffic can be explicitly routed and scheduled in software-defined networks, which allows us to more flexibly schedule traffic.

[0031] In this embodiment, a multi-objective disaster backup method between data centers based on reinforcement learning is disclosed. In the time-expanded network after the network between data centers is expanded, multicast routing and store-and-forward mechanisms are used to transmit backup data, thereby realizing The smallest total backup cost and load balance; use the multi-objective reinforcement learn...

Embodiment 2

[0073] In this embodiment, a multi-objective disaster backup system between data centers based on reinforcement learning is disclosed, including:

[0074] The acquisition module acquires the data to be backed up;

[0075] The storage module stores the time expansion network and the backup routing selection model, the backup routing selection model includes, the fitness function of each link in the multicast tree in the time expansion network to the multicast tree and the congestion factor function of each link, with the minimum The goal is to minimize the backup cost and link load balance, and obtain the optimal backup routing scheme;

[0076] The calculation module inputs the data to be backed up into the backup routing selection model to obtain the optimal backup routing scheme.

Embodiment 3

[0078] In this embodiment, a computer-readable storage medium is disclosed, which is used to store computer instructions. When the computer instructions are executed by a processor, the multi-objective disaster between data centers based on reinforcement learning described in Embodiment 1 is completed. Steps of the backup method.

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Abstract

The multi-objective disaster backup method and system between data centers based on reinforcement learning disclosed in this disclosure includes: expanding the inter-data center network to obtain time-expanded network; obtaining time-expanded data to be backed up in the network; inputting the data to be backed up into the backup routing selection model , to obtain the optimal backup routing scheme; the data to be backed up is transmitted and backed up in the time-expanded network with the optimal backup routing scheme; the backup routing selection model includes the adaptation of each link in the multicast tree in the time-expanded network to the multicast tree The degree function and the congestion factor function of each link, with the goal of minimizing the backup cost and link load balance, are solved to obtain the optimal backup routing scheme. During disaster backup, the factors of backup cost and load balance are fully considered, and the optimal backup routing scheme in terms of both backup cost and load balance is obtained through the backup routing selection model. The link is congested.

Description

technical field [0001] The present disclosure relates to a multi-objective disaster backup method and system between data centers based on reinforcement learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, many large enterprises, such as Amazon, Google, and Microsoft, have deployed large data centers in multiple geographical locations to provide various services to millions of users around the world. Due to natural disasters and man-made destruction, data security has attracted more and more attention. In order to achieve data redundancy and ensure data security, it is necessary to periodically replicate TB to PB data in the inter-data center network and distribute it to three or more other remote data centers, which is disaster backup. [0004] At present, for redundant disaster backup, most studies use multicast ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/707H04L12/703H04L12/801H04L12/803H04L45/24H04L45/28
CPCH04L45/22H04L45/28H04L45/24H04L47/12H04L47/125
Inventor 王华燕嘉鑫伊善文
Owner SHANDONG UNIV