A Congestion Control Method for Named Data Networks Based on Reinforcement Learning
A named data network and reinforcement learning technology, applied in the field of network information transmission and communication, to reduce the number of lost packets, avoid network congestion, and reduce the average network delay.
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[0066] The network topology of this embodiment is as follows Figure 4 shown. In the simulation setting, there are 9 network nodes in total, including 4 content requesters, 4 routers (intermediate forwarding nodes) and 1 content producer. The bandwidth and delay settings of each link are shown in Table 1. Router1 and Router2 have a relatively good and relatively poor output link respectively: Router1-Router4 is a better output link. Compared with Router1-Router3, this link has larger bandwidth and smaller delay , more data is transmitted per unit time. The two output links corresponding to Router2 are the same, but generally better than the output link of Router1.
[0067] Table 1 Link setting table
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[0069]
[0070] In this embodiment, the proposed intelligent forwarding strategy is compared with the best routing algorithm Best Route, the multi-path forwarding algorithm Multicast, and the request forwarding algorithm (Request Forwarding). In the simulation...
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