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
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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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