A neural network based congestion control algorithm for content-centric networks

Abstract

Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network.

Fuzzy Logic‐based Efficient Interest Forwarding (FLEIF) in Named Data Networking

“…This used an explicit notification to inform the sender about the available BW and uses Software Defined Networking to adopt the best link for Interest forwarding. Neural network algorithms are also proposed to control congestion explicitly . Byun et al proposed an adaptive Interest management scheme that is achieved by maintaining a balance in Interest forwarding according the link capacity.…”

A Neural Network Based Traffic-Aware Forwarding Strategy in Named Data Networking

“…The Usage of NN as a tool for traffic prediction has been prevalent in the computer network due to its ability to learn complicated patterns [9][10][11][12]. NN can be used for nexthop selection in NDN forwarding strategy [6]. The summary of the designed NN and its design motivations are described in this section.…”

“…As was discussed in [6], the mentioned input parameters include the factors which have an impact on the amount of load on the path in addition to the topology based parameters that influence congestion occurrence and link overload. Different scenarios were implemented to obtain the best collection of input parameters.…”

“…After few trials, between 10 to 50 times for each NN parameter, the neural network with 8 input neurons and 10 hidden layers with the sigmoid activation function and one neuron with a linear function in the output layer is selected to predict overload probability based on the analysis of MSE and regression curve in the different designs. The 8 NN inputs comprise network topology input parameters and load parameters for each link as follows [6] The data from different network topologies with different loads is used as a training data in the designed NN. The NN dataset is divided into three sets including 70% for training, 15% for validation, and 15% for testing data, in order to evaluate the accuracy of the designed NN.…”

“…GEANT is a real core network in Europe. This topology has been used in various evaluations of the NDN network [6,[19][20][21]. Table 2 gives information on the implemented scenario.…”